Method and system for mitigating interference relating to fixed interferers

ABSTRACT

Aspects of the subject disclosure may include, for example, obtaining information regarding an external source that might be affected by downlink (DL) transmissions emitted by an aggregation of modular antenna arrays, wherein each modular antenna array of the aggregation of modular antenna arrays comprises a set of antenna elements, resulting in multiple sets of antenna elements, and wherein the aggregation of modular antenna arrays is operated in multi-user (Mu)-multiple-input-multiple-output (MIMO) mode in which parallel transmissions are facilitated for a plurality of user equipment (UEs), determining adjustments for particular antenna elements of the multiple sets of antenna elements based on the obtaining the information, wherein the determining the adjustments is based on a probability that interference of the parallel transmissions with the external source will be mitigated, and causing the particular antenna elements to be operated based on the adjustments. Other embodiments are disclosed.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims priority to U.S. Provisional Ser. No.63/155,257, filed Mar. 1, 2021, and U.S. Provisional Ser. No.63/193,175, filed May 26, 2021. The contents of each of the foregoingare hereby incorporated by reference into this application as if setforth herein in full.

FIELD OF THE DISCLOSURE

The subject disclosure relates to mitigating interference relating tofixed interferers. The subject disclosure also relates to optimizing orimproving spectral efficiency using massivemultiple-input-multiple-output (MIMO), including single-user (Su)-and/or multi-user (Mu)-MIMO, with aggregated modular adaptive antennaarrays/panels.

BACKGROUND

As the number of mobile users and wireless applications continues togrow at a rapid rate, efficient use and management of wireless frequencyspectrum becomes increasingly important, especially in cases wherespectrum is limited or deficient.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1A is a block diagram illustrating an exemplary, non-limitingembodiment of a communications network in accordance with variousaspects described herein.

FIG. 1B is a block diagram illustrating an exemplary, non-limitingembodiment of a system functioning within, or operatively overlaid upon,the communications network of FIG. 1A in accordance with various aspectsdescribed herein.

FIG. 2A depicts an exemplary, non-limiting embodiment of a modularantenna array in accordance with various aspects described herein.

FIG. 2B depicts an example deployment of multiple instances of themodular antenna array of FIG. 2A along with other antennas in accordancewith various aspects described herein.

FIGS. 2C and 2D each depicts an example arrangement of multipleinstances of the modular antenna array of FIG. 2A in accordance withvarious aspects described herein.

FIG. 3 is a diagram illustrating an exemplary, non-limiting embodimentof multi-array/panel antenna calibration in accordance with variousaspects described herein.

FIG. 4A is a diagram illustrating exemplary, non-limiting coherenceblock-related graphs in accordance with various aspects describedherein.

FIG. 4B is a diagram illustrating the benefits of using orthogonal pilotsequences in accordance with various aspects described herein.

FIGS. 4C and 4D are diagrams illustrating example interferencemitigation techniques in accordance with various aspects describedherein.

FIG. 4E is a diagram illustrating an exemplary, non-limiting system forbeam optimization in accordance with various aspects described herein.

FIG. 5A depicts an exemplary, non-limiting example of multiple userequipment (UEs) being served in a cell in accordance with variousaspects described herein.

FIG. 5B depicts an exemplary, non-limiting example of a scalablearchitecture of aggregated (coherent) modular antenna arrays andclustered processing units in accordance with various aspects describedherein.

FIG. 6 shows exemplary, non-limiting equations that can be applied toimplement FDD Mu-MIMO in accordance with various aspects describedherein.

FIG. 7 depicts an illustrative embodiment of a method for FDD Mu-MIMO inaccordance with various aspects described herein.

FIG. 7A depicts a table and example equations that can be applied tomitigate various types of input interference in accordance with variousaspects described herein.

FIG. 7B is a diagram of example antenna elements of a modular antennaarray in which a phase difference between uplink and downlink signals inFDD may be compensated in accordance with various aspects describedherein.

FIG. 8A depicts a table identifying monitorable parameters forfacilitating MIMO networking in accordance with various aspectsdescribed herein.

FIG. 8B depicts a table identifying controllable parameters forfacilitating MIMO networking in accordance with various aspectsdescribed herein.

FIGS. 8C-8H, 8J-8N, and 8P-8R each depicts an illustrative embodiment ofa method in accordance with various aspects described herein.

FIG. 9 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communications network in accordance withvarious aspects described herein.

FIG. 10 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 11 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 12 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

Having access to a larger wireless frequency spectrum provides a mobilenetwork operator with improved network coverage and speed as well asincreased capacity to serve more users. In cases where spectrum islimited or deficient, however, it can be difficult to efficientlyservice growing user bases without overloading the network. For example,in some instances, a mobile network operator may only have access tolimited portions of one or more frequency bands—e.g., from about 1.7gigahertz (GHz) to about 2.5 GHz in the Mid-band and from about 3.7 GHzto about 4.2 GHz in the C-band—for carrying all of the user traffic onthe operator's mobile network. Expanding the capacity of such a networkmay require creative and efficient use and management of the limitedspectrum. In the example above, one solution for addressing poor ULcoverage in the C-band (which may have adequate capacity and sufficientDL coverage) might be to utilize the Mid-band for most of the ULoperations, at least for UEs that are beyond the UL coverage area in theC-band. While such an implementation may supplement UL coverage of theC-band, it can be a zero sum solution, since the Mid-band would beburdened with nearly all of the UL traffic (particularly in areasfarther away from a base station or tower) and thus experience areduction in UL capacity.

The subject disclosure describes, among other things, illustrativeembodiments of a network implementation that is capable of extending thecapacity of an available band of spectrum (e.g., in frequency divisionduplex (FDD) and/or time division duplex (TDD)) using Mu-MIMO. Invarious embodiments, this may be augmented by leveraging aggregated orcombined modular adaptive/active/advanced antenna systems (AAS) orarrays. In exemplary embodiments, Mu-MIMO, for example, may be employedin a lower frequency band, such as the Mid-band or the like, which canenable improved UL signaling for UEs since lower frequencies can carrysignals for longer physical distances and thus allow for increasedcoverage. In a case where a mobile network employs portions of multiplebands for network operations, such as, for example, the C-band and theMid-band, and where the UL of the lower band (e.g., the Mid-band) isused not only for its own UL traffic but also the UL traffic associatedwith the higher band (e.g., the C-band), leveraging Mu-MIMO in the lowerband can improve the capacity of its UL, and can thus restore ULcapacity to the higher band. In one or more embodiments, the networkimplementation enables harnessing or “slicing” of an available band ofspectrum (e.g., in FDD and/or TDD) to serve/accommodate certain selectusers or user equipment (UEs), such as stationary (or near stationary),line of sight (LOS) (or near LOS), or fixed wireless UEs/customerpremises equipment (CPEs) that have projected data rate requirements.For example, in certain embodiments, a dedicated channel (e.g., a 20 MHzchannel or the like) may be assigned to support such UEs/CPEs in Mu-MIMOmode.

In exemplary embodiments, the network implementation, equipped withcombined modular antenna arrays that include antenna elements havinglarger apertures, is capable of selectively applying Mu-MIMO for UEsassociated with large coherence blocks (e.g., coherence blocks thatexceed a threshold). These may include, for example, stationary (or nearstationary) UEs, UEs with LOS (or near LOS), or fixed wireless UEs orCPEs, where associated buffers may be continuously (or nearcontinuously) full or near full. In various embodiments, coherenceblocks may be exploited, as described herein, to control inter-cellinterference (e.g., via pilot signal distribution/control) and tomaximize UL coverage for sounding reference signal (SRS) purposes (e.g.,by averaging SRS over large coherence blocks). In various embodiments,UEs with smaller coherence blocks (e.g., non-stationary UEs or UEs withnon-line of sight (NLOS)) may be configured for Su-MIMO, where thevarious UEs in Su- and Mu-MIMO modes may be supported via appropriatescheduling of time slots.

In exemplary embodiments, an aggregation or combination of modularadaptive arrays (e.g., a radio unit (RU)) may include multiple antennapanels that, as a group, function as a “coherent” antenna system. Invarious embodiments, an antenna panel may have columns and rows (e.g.,16 columns and 6 rows or the like) of antenna elements (which may alsobe referred to herein as a T/R or T/R element, individually, or as T/Rsor T/R elements, in the plural) that may, for example, be dual-polarized(e.g., at +45 degrees and −45 degrees or the like). Each antenna elementmay be weightable/weighted with amplitude and phase, where the antennaelements, as a group, may be capable of supporting numerous layers(e.g., simultaneous data streams for multiple UEs) in the UL (e.g., 8layers or more) and the DL (e.g., 16 layers or more). In exemplaryembodiments, modular antenna panels can have a larger aperture relativeto conventional antennas, which may enable sharper beamforming. Invarious embodiments, an antenna panel may employ advanced semiconductortechnologies (e.g., radio frequency (RF) complementarymetal-oxide-semiconductor (CMOS) technology), which can reduce basestation power and costs. Additionally, an antenna panel may also beconfigured to provide higher radiated power from each antenna element(e.g., higher Effective, or Equivalent, Isotropically Radiated Power(EIRP)) than conventional antennas, and can do so at lower powerconsumption than conventional antennas, which eliminates a need forextra tower power cabling.

Aggregating or combining active antenna panels provides for widerantenna configurations, which enables flexible beam formation withincreased resolution. This expands capacity across (e.g., all existing)locations or positions of antenna systems or tower tops of a mobilenetwork, since beams can be formed for, and directed/steered to, eventhose UEs that, from the perspective of the combined modular antennaarray, are separated only by a small distance (e.g., less than about 5,7, 10, or 15 degrees apart or the like), and signals exchanged with suchUEs can all be transmitted at the same frequency and time using MIMOtechniques. In exemplary embodiments, modular antenna arrays may bestackable or arrangeable in different orientations to attain narrowerbeams in desired directions. For example, arranging modular antennaarrays horizontally allows for narrower beams in an azimuth direction,and arranging them vertically allows for narrower beams in an elevationdirection.

As more unmanned aerial vehicles (UAVs), such as drones, become deployedfor extended network coverage, their activities can interfere withoperations of cell towers depending on their locations relative to thetowers. It may thus be crucial for a tower or base station toidentify/track UAV locations to determine appropriate null patterns andbeam directions. Conventional active antennas employ components thathave extensive power requirements, and rely on sub-arrays, where only asubset of available antenna elements is programmable. This limits theability of the system to adapt phase/amplitude of the antennas andrestricts scanning in the elevation direction, which may be needed totrack UAVs. Exemplary embodiments of the modular antenna array arecapable of steering beams in both the azimuth and elevation directions.In various embodiments, the modular antenna array may include arespective programmable device for each antenna element of the array,which allows for wide elevation scanning (e.g., −20 degrees to 50degrees or the like). Coupled with advanced semiconductor (e.g., RFCMOS) technology, for example, the modular antenna array can enableimproved nulling (e.g., nulling out of signals to/from drones) andminimization of grating lobes.

Embodiments, described herein, also enable the aggregation of modularantenna arrays (which are high transmit/receive (T/R)) to transparentlyserve some UEs in Su-MIMO mode and other UEs in Mu-MIMO mode. In variousembodiments, reciprocity-based channel estimation may be employed, andaggregations of modular antenna arrays may be logicallyconfigured/controlled such that DL channel state information(CSI)-reference signal (RS) overhead is reduced for UEs in Su-MIMO mode,which enables such UEs to operate unaware of the large quantities ofavailable antenna elements that are employed to service UEs in both Su-and Mu-MIMO modes. The added capability to support multiple UEssimultaneously in Mu-MIMO mode (i.e., multiple parallel transmissions),and not just a single UE at a time, can dramatically improve capacity.

Various embodiments, described herein, also provide for calibration ofaggregated or combined antenna panels, which enables all or selectedgroups of the panels to function as a coherent antenna system for sharpbeamforming and steering. Exemplary processes for determining weightsfor antenna elements (e.g., based on SRS or other channel information)are also described herein for FDD and TDD, including algorithms thatexploit the spatial scenario of mobile UEs to extract a spatial channel(e.g., without complications of a microscopic fading channel) for the DLin FDD based on FDD UL estimates performed for a different FDDfrequency. Various embodiments for addressing interference are alsodescribed herein.

Augmenting a mobile network system with the addition of aggregated orcombined antenna panels (with larger antenna element apertures), andleveraging such panels to harness an available band of spectrum (e.g.,in FDD and/or TDD and for Su- and/or Mu-MIMO), as described herein,reduces or eliminates a need for a mobile network operator to obtain oracquire additional spectrum, which can provide significant cost savings(e.g., hundreds of millions of dollars or more per MHz).

In various embodiments, the network implementation may operate inaccordance with open standards, such as Open Radio Access Network(O-RAN) standards, which obviates equipment incompatibilities andconflicts between equipment vendors.

One or more aspects of the subject disclosure include a non-transitorymachine-readable medium, comprising executable instructions that, whenexecuted by a processing system operatively coupled to an aggregation ofmodular antenna arrays and including a processor, facilitate performanceof operations. The operations can include obtaining informationregarding an external source that might be affected by downlink (DL)transmissions emitted by the aggregation of modular antenna arrays,wherein each modular antenna array of the aggregation of modular antennaarrays comprises a set of antenna elements, resulting in multiple setsof antenna elements, and wherein the aggregation of modular antennaarrays is operated in multi-user (Mu)-multiple-input-multiple-output(MIMO) mode in which parallel transmissions are facilitated for aplurality of user equipment (UEs). Further, the operations can includedetermining adjustments for particular antenna elements of the multiplesets of antenna elements based on the obtaining the information, whereinthe determining the adjustments is based on a probability thatinterference of the parallel transmissions with the external source willbe mitigated. Further, the operations can include causing the particularantenna elements to be operated based on the adjustments.

One or more aspects of the subject disclosure include a device,comprising a processing system including a processor, wherein theprocessing system is communicatively coupled with a plurality ofcoherent modular antenna panels, wherein each modular antenna panel ofthe plurality of coherent modular antenna panels comprises a group ofantenna elements, resulting in multiple groups of antenna elements, andwherein the plurality of coherent modular antenna panels is operated infrequency division duplex (FDD) multi-user(Mu)-multiple-input-multiple-output (MIMO) in which paralleltransmissions are facilitated for a plurality of user equipment (UEs);and a memory that stores executable instructions that, when executed bythe processing system, facilitate performance of operations. Theoperations can include detecting an external noise source. Further, theoperations can include identifying precoding for particular antennaelements of the multiple groups of antenna elements based on thedetecting the external noise source. Further, the operations can includeoperating the particular antenna elements based on the precoding whenfacilitating the parallel transmissions for the plurality of UEs.

One or more aspects of the subject disclosure include a method. Themethod can include receiving, by a processing system including aprocessor, data regarding an external noise source that is affected byout-of-band downlink (DL) emissions radiated by a coherent combinationof modular antenna arrays, wherein each modular antenna array of thecoherent combination of modular antenna arrays comprises a group ofantenna elements, resulting in multiple groups of antenna elements, andwherein the coherent combination of modular antenna arrays is beingoperated in multi-user (Mu)-multiple-input-multiple-output (MIMO) modein which parallel transmissions are facilitated for a plurality of userequipment (UEs). Further, the method can include identifying, by theprocessing system, adjustments for select antenna elements of themultiple groups of antenna elements based on the receiving the data.Further, the method can include causing, by the processing system, theselect antenna elements to be operated based on the adjustments suchthat the parallel transmissions facilitated for the plurality of UEs aresteered away from the external noise source.

Other embodiments are described in the subject disclosure.

Referring now to FIG. 1A, a block diagram is shown illustrating anexample, non-limiting embodiment of a system/communications network 100in accordance with various aspects described herein. For example, system100 can, in whole or in part, facilitate optimization or improvement ofservice quality and/or capacity in a MIMO network supported byaggregations of modular antenna arrays and/or facilitate mitigatinginterference relating to fixed interferers. In particular, acommunications network 125 is presented for providing broadband access110 to a plurality of data terminals 114 via access terminal 112,wireless access 120 to a plurality of mobile devices 124 and vehicle 126via base station or access point 122, voice access 130 to a plurality oftelephony devices 134, via switching device 132 and/or media access 140to a plurality of audio/video display devices 144 via media terminal142. In addition, communications network 125 is coupled to one or morecontent sources 159 of audio, video, graphics, text and/or other media.While broadband access 110, wireless access 120, voice access 130 andmedia access 140 are shown separately, one or more of these forms ofaccess can be combined to provide multiple access services to a singleclient device (e.g., mobile devices 124 can receive media content viamedia terminal 142, data terminal 114 can be provided voice access viaswitching device 132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 159. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 159 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

FIG. 1B is a block diagram illustrating an example, non-limitingembodiment of a system 160 functioning within, or operatively overlaidupon, the communications network 100 of FIG. 1A in accordance withvarious aspects described herein. For example, system 160 can, in wholeor in part, facilitate optimization or improvement of service qualityand/or capacity in a MIMO network supported by aggregations of modularantenna arrays and/or facilitate mitigating interference relating tofixed interferers. In some embodiments, the system 160 may correspondto, or include, one or more networks (e.g., a communications network, adata network, etc.).

As shown in FIG. 1B, the system 160 may include a RAN 162 acommunicatively coupled to a core network 190. The core network 190 caninclude a 5G network, an evolved packet core (EPC) network, a highergeneration network, or any combination thereof. In various embodiments,the RAN 162 a may be, or may include, a vRAN (e.g., in an Open RAN(O-RAN) implementation), in which software is decoupled from hardware,and implementation thereof is in accordance with principles of networkfunction virtualization (NFV), where the control plane is separated fromthe data plane. The vRAN may include a centralized set of baseband unitslocated remotely from antennas and remote radio units, and may beconfigured to share signaling amongst cells. In various embodiments, thevRAN may provide control and service delivery optimization functions aswell as SRS and pilot signals to ensure orthogonality across differentcells/sites to prevent pilot contamination and subsequent inter-cellinterference.

As shown in FIG. 1B, the RAN 162 a may include a network servicemanagement platform 163 a and a RAN intelligent controller (RIC) 164 a.The RIC 164 a may include a RIC portion 164 a-1 implemented, orotherwise incorporated, in the network service management platform 163a. The RIC 164 a may include a RIC portion 164 a-2 having a control orcentralized unit (CU) 174 a (e.g., a base station CU, such as a gNodeB(gNB) CU or the like) that provides a CU applications layer 176 a aswell as a CU control plane CU-CP and a CU user plane CU-UP (e.g.,represented as CU-CP & CU-UP 178 a). In various embodiments, the RICportion 164 a-1 may be configured to operate in non-real-time, and theRIC portion 164 a-2 may be configured to operate in near real-time. Theparticular functions performed by the RIC portions 164 a-1, 2 can varybased on various criteria, including implementing changing parameters orrequirements for the network, and can also include redundancy and/ordynamic switching of functions (including functions described herein)between the RIC portions 164 a-1, 2.

As shown in FIG. 1B, the RAN 162 a may include distributed units (DUs)166 a-1 through 166 a-L (L≥1) (hereinafter referred to collectively as“DUs 166 a,” and individually as “DU 166 a”). In various embodiments,the DUs 166 a may include baseband units (e.g., base station DUs, suchas gNB DUs or the like) configured to perform signal processing, UEscheduling, and/or the like. In exemplary embodiments, each of one ormore DUs 166 a may be implemented as a virtual DU (vDU). The RAN 162 amay also include remote radio heads or remote units (RUs) 168 a-1through 168 a-M (M≥1) (hereinafter referred to collectively as “RUs 168a,” and individually as “RU 168 a”). The RUs 168 a may communicativelycouple (e.g., via an air interface) with user equipment (UEs) 170 a-1through 170 a-N (N≥1) (hereinafter referred to collectively as “UEs 170a,” and individually as “UE 170 a”). In various embodiments, the RUs 168a may include remote radio units, antennas, and/or the like. As shown inFIG. 1B, the RUs 168 a, the DUs 166 a, and the CU 174 a may, by way of afronthaul 181 a, a midhaul 182 a, and a backhaul 183 a, provide (e.g.,controlled) connectivity between the core network 190 and the UEs 170 a.In one or more embodiments, the fronthaul 181 a, the midhaul 182 a,and/or the backhaul 183 a may conform to open standards, such as O-RANstandards or the like.

In exemplary embodiments, each of one or more RUs 168 a may include oneor more aggregations of modular antenna arrays/panels. As described inmore detail below with respect to FIGS. 2A-2D, a modular antenna panelmay include multiple antenna elements, where a combination of themultiple antenna elements of (e.g., all of) the modular antenna panelsin the aggregation enables the modular antenna panels to function as acoherent antenna system (e.g., where all the antenna elements of all thepanels are synchronized in frequency and phase for every cycle). Invarious embodiments, a modular antenna array may enable employment ofMIMO techniques, such as Su- and/or Mu-MIMO, as described herein.

Although FIG. 1B illustrates the CU 174 a as being incorporated in theRIC portion 164 a-2, in various embodiments, the CU 174 a may beimplemented as a distinct component from the RIC portion 164 a-2. Insome embodiments, the RIC 164 a and the network service managementplatform 163 a may operate as part of one or more central control planesthat oversee a geographic region that can include multiple (e.g.,hundreds, thousands, etc.) of remote units, distributed units,centralized units, or any combination thereof.

In various embodiments, the system 160 may be functionally separated orsegmented in accordance with one or more time-based zones or frames. Forexample, the network service management platform 163 a and/or the RICportion 164 a-1 may be operative at or in non-real-time; the RIC portion164 a-2 and/or the CU 174 a may be operative at or in near-real-time;and the DUs 166 a, the RUs 168 a, and/or the UEs 170 a may be operativeat or in real-time. As the terms (and related terms) are used herein,real-time operations may occur over a span of fractions of a second upto a second (or the like), near-real-time operations may occur over thecourse of a few seconds (e.g., 1 to 5 seconds or the like), andnon-real-time operations may occur over a time period that is greaterthan a few seconds (e.g., greater than 5 seconds or the like).

In various embodiments, the network service management platform 163 amay manage, or otherwise adapt, RIC behaviors and/or operations acrossone or more of the three time zones or timeframes described above (e.g.,real-time, near-real-time, and non-real-time) on an individualizedand/or collective basis. Such management or adaptation of RIC behaviorsand/or operations may conform to one or more models or microservices(e.g., artificial intelligence (AI) models or microservices), or networkapplications (e.g., rAPPs, xAPPs), as described herein. In turn, the RICmay establish and/or modify policies and/or behaviors of respective CUs,DUs, and RUs in accordance with the model(s) or microservice(s). In thisregard, the network service management platform 163 a may indirectlyinfluence the behaviors and/or operations of CUs, DUs, and/or RUs viaone or more RICs.

In some embodiments, the communication channels and/or links between theRAN 162 a and the UEs 170 a may include wireless links. In variousembodiments, some or all of the UEs 170 a may be mobile, and maytherefore enter and/or exit a service or coverage area associated withthe RIC 164 a. In various embodiments, some of the UEs 170 a may includenon-mobile or stationary devices. In some of these embodiments, the RAN162 a may include one or more routers, gateways, modems, cables, wires,and/or the like, and the communication channels and/or links between theRAN 162 a and such UEs may include wired/wireline links, optical links,etc.

In various embodiments, a RIC (e.g., the RIC portions 164 a-1, 2 of theRIC 164 a) may store, execute, and/or deploy applications ormicroservices that are configured to control and manage a RAN (e.g., theRAN 162 a). In one or more embodiments, for example, the RIC portion 164a-1 may store, execute, and/or deploy rApps, and the RIC portion 164 a-2may store, execute, and/or deploy xApps (e.g., in or via an applicationslayer, such as the CU applications layer 176 a). The applications ormicroservices may relate to scheduler capacity optimization, coverageoptimization, capacity optimization (including, for example, viainterference mitigation), user quality optimization (including, forexample, for the UL and/or the DL), radio connection management,mobility management, quality-of-service (QoS) management, interferencemanagement, telemetry, network traffic control and/or management, deviceadmissions (e.g., UE admissions control), and/or the like. In variousembodiments, an application may include one or more models, such as AI(e.g., machine learning (ML)) models, that when executed in one or morecontainers, provide corresponding microservices. Deployment of an AImodel in a RIC (or, more generally, a RAN) may involve, or include, forexample, executing or instantiating the AI model in one or morecontainers in the RIC portion 164 a-1 and/or the applications layer ofthe RIC portion 164 a-2 (e.g., the CU applications layer 176 a), suchthat the AI model processes inputs (e.g., received from othermicroservices running on the RIC and/or from various components of theRAN, such as the CU-CP & CU-UP 178 a, the DUs 166 a, and/or the RUs 168a) and provides outputs (e.g., to the other microservices and/or thevarious components of the RAN), in accordance with the AI model, tocontrol the overall operation of the RAN.

It is to be appreciated and understood that the system 160 can includevarious quantities of cells (e.g., primary cells (Pcells) and/orsecondary cells (Scells)), various quantities of network nodes in acell, and/or various types of network nodes and/or cells (e.g.,heterogeneous cells, etc.).

It is also to be appreciated and understood that the quantity andarrangement of systems, networks, platforms, controllers, controllerportions, centralized units, applications layers, distributed units,remote units, fronthauls, midhauls, backhauls, and/or antenna arraysshown in FIG. 1B are provided as an example. In practice, there may beadditional systems, networks, platforms, controllers, controllerportions, centralized units, applications layers, distributed units,remote units, fronthauls, midhauls, backhauls, and/or antenna arraysthan those shown in FIG. 1B. For example, the system 160 can includemore or fewer systems, networks, platforms, controllers, controllerportions, centralized units, applications layers, distributed units,remote units, fronthauls, midhauls, backhauls, and/or antenna arrays.Furthermore, two or more systems, networks, platforms, controllers,controller portions, centralized units, applications layers, distributedunits, remote units, fronthauls, midhauls, backhauls, or antenna arraysshown in FIG. 1B may be implemented within a single system, network,platform, controller, controller portion, centralized unit, applicationslayer, distributed unit, remote unit, fronthaul, midhaul, backhaul, orantenna array shown in FIG. 1B or a single system, network, platform,controller, controller portion, centralized unit, applications layer,distributed unit, remote unit, fronthaul, midhaul, backhaul, or antennaarray shown in FIG. 1B may be implemented as multiple, distributedsystems, networks, platforms, controllers, controller portions,centralized units, applications layers, distributed units, remote units,fronthauls, midhauls, backhauls, or antenna arrays. Additionally, oralternatively, a set of systems, networks, platforms, controllers,controller portions, centralized units, applications layers, distributedunits, remote units, fronthauls, midhauls, backhauls, and/or antennaarrays (e.g., one or more systems, networks, platforms, controllers,controller portions, centralized units, applications layers, distributedunits, remote units, fronthauls, midhauls, backhauls, and/or antennaarrays) of the system 160 may perform one or more functions described asbeing performed by another set of systems, networks, platforms,controllers, controller portions, centralized units, applicationslayers, distributed units, remote units, fronthauls, midhauls,backhauls, and/or antenna arrays of the system 160.

FIG. 2A depicts an example, non-limiting embodiment of a modular antennaarray/panel 200 in accordance with various aspects described herein. Inexemplary embodiments, the modular antenna array 200 may be a modularactive/adaptive antenna system. As depicted, the modular antenna array200 may be rectangular, and may include multiple columns and rows ofantenna elements 202. For example, as shown, the modular antenna array200 may include sixteen columns and six rows of antenna elements 202,and may have a width of about 40 inches and a height of about 21 inches(with a surface area of about 840 inches²). It is to be appreciated andunderstood that the modular antenna array 200 and/or the antennaelements 202 therein may be any shape or combination of shapes with anysuitable dimensions, and the modular antenna array 200 may include anysuitable numbers of columns and rows of antenna elements 202.

The antenna elements 202 may employ any suitable type of antennatechnology. In exemplary embodiments, one or more (e.g., each) of theantenna elements may employ advanced RF semiconductor technology (e.g.,RF CMOS technology) to avoid excessive power requirements. In one ormore embodiments, each antenna element 202 may be weightable withamplitude and phase, where the antenna elements 202, as a group, maysupport numerous layers (e.g., simultaneous data streams intended formultiple UEs) in the DL (e.g., 32 layers or more) and the UL (e.g., 16layers or more). In various embodiments, the shape, dimensions, and/orthe number/type of antenna elements and application of various T/Rs of amodular antenna array 200 may be selected in accordance with variousaspects described herein, including, for example, to enable (e.g.,operative) aggregating of multiple modular antenna arrays 200 that, incombination, function as a “coherent” antenna system capable ofproviding improved beamforming capabilities and supporting variouscommunication schemes, such as MIMO (e.g., Su- and/or Mu-MIMO).

FIG. 2B depicts an example deployment 210 of multiple instances of themodular antenna array 200 of FIG. 2A (shown in combination as modularAAS 200 x and individually as modular antenna arrays 200 a, 200 b, 200c, and 200 d) along with other antennas in accordance with variousaspects described herein. In various embodiments, example deployment 210may be disposed on, or otherwise mounted to, a tower (e.g., at a towertop) (not shown). As depicted, the modular antenna arrays 200 a-200 dmay be arranged among other types of antennas, such as conventional,narrower passive antennas 211, a Universal Mobile TelecommunicationsSystem (UMTS) antenna, and a C-band AAS. Here, the modular antennaarrays may be operatively aggregated or combined to function as acoherent antenna system. For example, one or more of the modular antennaarrays 200 a-200 d may be combined with other(s) of the modular antennaarrays 200 a-200 d to provide coordinated beamforming and beamsteering.Given the modular nature of the arrays 200, new arrays or panels may beadded to a deployment (possibly with additional processing power adds orthe like, such as at a conveniently located vRAN DU 166 a and/or a CU174 a of FIG. 1B, as needed) to seamlessly increase capacity without aneed for an overhaul, removal, or a reconfiguration of antennas at atower top. In some embodiments, one or more of modular antenna arrays200 a, 200 b, 200 c, and 200 d shown in FIG. 1B may be operated incombination (i.e., as a coherent antenna system) with one or more othermodular antenna arrays 200 a-200 d located in a different cell site. Inthis way, modular antenna arrays, which may not be co-located, maynevertheless be operatively aggregated or combined to function as acoherent antenna system.

It is to be appreciated and understood that the modular antenna arrays200 a-200 d may be arranged with one or more other antennas in anysuitable manner. In certain embodiments, one or more of the modularantenna arrays 200 a-200 d may be aggregated or combined with one ormore other types of antennas (such as those shown in FIG. 2B) tofunction as a coherent antenna system.

In some embodiments, a distance, or spacing, between the various modularantenna arrays 200 a-200 d may necessitate different spatial samplingrates associated with the various antenna elements of the modularantenna arrays 200 a-200 d in order to accommodate situations whereantenna elements of different arrays have differing UL and DLfrequencies in order to maintain the same physical angle ofarrivals/departures. In various embodiments, aggregating modular antennaarrays with certain separation between arrays may enable beamformingthat might not be possible with a single antenna array having the samesurface area as the aggregated modular antenna arrays.

FIGS. 2C and 2D each depicts an example aggregation (or combination) ofmultiple instances of the modular antenna array 200 of FIG. 2A inaccordance with various aspects described herein. As depicted in FIG.2C, modular antenna arrays 200 a, 200 b may be aggregated (e.g.,stacked) in a vertical direction relative to up orientation 215.Combining the modular antenna arrays 200 a, 200 b in such a manner mayenable improved beamforming/beamsteering in the vertical direction(e.g., as shown by beams 222, 224, and 226). As depicted in FIG. 2D,modular antenna arrays 200 c, 200 d may be arranged (e.g., stacked) in ahorizontal direction relative to up orientation 216. Orienting themodular antenna arrays 200 c, 200 d in such a manner may enable improvedbeamforming/beamsteering in the horizontal direction (e.g., as shown bybeams 232, 234, and 236). In various embodiments, the modular antennaarrays 200 a-200 d may be operatively combined together, with a verticalarrangement of arrays 200 a, 200 b and a horizontal arrangement ofarrays 200 c, 200 d (e.g., as shown in FIG. 2B), to enable improvedbeamforming/beamsteering in both the vertical and horizontal directions,depending on the needs of the particular site geometry and user locationdistribution.

In exemplary embodiments, aggregations of modular antenna arrays 200 maybe arranged and mounted on a tower as lightweight modules, where modularbeamforming and signal processing systems (which may include, forexample, commercial off-the-shelf (COTs) devices, hybrid COTS devices,and application-specific integrated circuit (ASIC) daughter cards,and/or the like) may be located at a concentration point or hub, such asa centralized RAN (C-RAN) or the like. In such embodiments, the modularantenna arrays 200 may be communicatively coupled to DUs/CUs (orvDUs/vCUs, such as vDUs 166 a and/or vCUs 174 a) via a (e.g.,preferably) open standard fronthaul (e.g., fronthaul 181 a). Forexample, in various embodiments, aggregations of modular antenna arrays200 may correspond to the RUs 168 a. In various embodiments, there maybe minimal associated physical layer 1 electronics (e.g., Low Phyelectronics) and/or RF electronics disposed on the tower. Centralizingsignal processing power away from the tower reduces or eliminates a needto perform tower top maintenance/replacements and allows opportunitiesfor simple upgrade to more advanced (e.g., lower power and higherperformance) semiconductor technologies every eighteen months or socycle.

FIG. 3 is a diagram illustrating an exemplary, non-limiting embodimentof multi-array/panel antenna calibration/recalibration in accordancewith various aspects described herein. As shown in FIG. 3, multiplemodular antenna arrays 200 (numbered 200-1 to 200-N) may be aggregatedor combined to function or operate as a coherent antenna system 201.Although not shown in FIG. 3, each antenna element of each of themodular antenna arrays 200-1 through 200-N may be communicativelycoupled with a respective transmitter (Tx) device (such as, e.g.,digital Tx 345 or the like) for the DL and a respective receiver (Rx)device (such as, e.g., digital Rx 346 or the like) for the UL. Inexemplary embodiments, various aspects of the calibration/recalibrationmay be applied for modular antenna arrays employed for TDD or FDD.

In exemplary embodiments, calibration/recalibration may be performed bya calibration function/device/system 350, which may be implemented inthe system 162 a (e.g., in a base station that includes the vDUs 166 aand/or the vCUs 174 a). In various embodiments, thecalibration/recalibration process may begin by (or involve) identifyinga reference antenna element (e.g., reference antenna element 200-1 a ofmodular antenna array 200-1, although any antenna element of modularantenna array 200-1 may be used as a reference antenna element). Arespective Rx (UL) phase offset and a respective Tx (DL) phase offset(e.g., for each of one or more tone frequencies) associated with eachother antenna element of the modular antenna array 200-1, relative tothe reference antenna element 200-1 a, may then be determined so as toidentify or “unwrap” phase amounts related to known propagation delays.In various embodiments, the calibration function/device/system 350 maycause the digital Tx 345 to transmit an Rx reference signal (e.g., asine wave at the Rx frequency for, say, FDD) in order to measure the Rx(UL) delay of each of the other antenna elements of the modular antennaarray 200-1. The Rx reference signal may experience an analog transmitdelay, which may be associated with conversion filters and/or otherelectronic devices related to the reference antenna element 200-1 a.Each other antenna element of modular antenna array 200-1, such asantenna elements 200-1 b, 200-1 c, 200-1 d, etc., may receive the Rxreference signal at a respective (e.g., known or constant) inter-elementpropagation delay, which may be based on the (e.g., known or fixed)distance between that antenna element and the reference antenna element200-1 a and/or based on properties of the material(s) of the antennaelements. As shown in FIG. 3, there may also be an analog receive delay(which may be associated with conversion filters and/or other electronicdevices) prior to receipt of the Rx reference signal at each digital Rx346. Given that the reference antenna element 200-la's analog transmitdelay is common to all of the other antenna elements for purposes of theRx reference signal, the phase offset (i.e., the UL delay) of the Rxreference signal received by each of the other antenna elements ofmodular antenna array 200-1, relative to the reference antenna element200-la, can be based on a difference between a total duration (fromtransmission of the Rx reference signal to receipt of the Rx referencesignal) and the known inter-element propagation delay between thatantenna element and the reference antenna element 200-1 a. Here, thecalibration function/device/system 350 may thus determine the respectivephase offsets (UL delays) for all of the other antenna elements,relative to the reference antenna element 200-1 a, accordingly, and canuse these respective phase offsets as part of calibrating the referenceantenna element 200-la and those other antenna elements.

The calibration function/device/system 350 may cause each of the otherantenna elements of modular antenna array 200-1 to transmit a Txreference signal (e.g., one at a time) to the reference antenna element200-la in order to determine the relative Tx (DL) delay associated withthat other antenna element. Here, for each of those other antennaelements, the corresponding Tx reference signal may experience an analogtransmit delay (which may be associated with conversion filters and/orother electronic devices related to that antenna element), and thereference antenna element 200-la may receive the Tx reference signal ata respective (e.g., known or constant) inter-element propagation delay(which may be based on the (e.g., known or fixed) distance between thatantenna element and the reference antenna element 200-1 a). Given thatthe analog receive delay associated with the reference antenna element200-la is common to all of the other antenna elements for purposes ofthe Tx reference signals, the phase offset (i.e., the DL delay) of theTx reference signal transmitted by each of those other antenna elements,relative to the reference antenna element 200-1 a, can be based on adifference between a total duration (from transmission of the Txreference signal to receipt of the Tx reference signal) and the knowninter-element propagation delay between that antenna element and thereference antenna element 200-la. Here, the calibrationfunction/device/system 350 may thus determine the respective phaseoffsets (DL delays) for each of the other antenna elements, relative tothe reference antenna element 200-1 a, accordingly, and can use theserespective phase offsets as part of calibrating the reference antennaelement 200-la and those other antenna elements.

In exemplary embodiments, for FDD and/or TDD, the calibrationfunction/device/system 350 may calibrate the various UL delays and DLdelays with one another, such that the relative delays between the ULand the DL are zero (or near zero), so as to overall calibrate the ULwith the DL. Additionally, for FDD, transmissions of reference signalsmay need to be in the proper frequency of the intended receiver. Forexample, for FDD, the calibration function/device/system 350 may causethe digital Tx 345 (the DL) to switch to a frequency, at which thevarious digital Rx 346's (the UL) may be configured to receive, prior totransmitting the above-described Rx and/or Tx reference signals.

Calibration/recalibration, as described herein, can thus facilitatecoherent beamforming and beamsteering (including, for example, for nullpatterns) among all of the antenna elements of the modular antenna array200-1.

It is to be appreciated and understood that amplitude (e.g., associatedwith the various Tx and Rx reference signals) can also be measured andused in the calibration process described above. Furthermore, the sameor a similar process may be used to calibrate the antenna elements ofevery other modular antenna array (e.g., modular antenna array 200-N,etc.) that is (or is to be) aggregated/combined with modular antennaarray 200-1.

Depending on how the modular antenna arrays 200-1 through 200-N areinstalled/oriented relative to one another, antenna elements of thesedifferent arrays may be arbitrarily offset from one another inthree-dimensional (3D) space. In exemplary embodiments, the calibrationfunction/device/system 350 may determine the installation geometry ofmodular antenna array 200-N, relative to modular antenna array 200-1,via time difference of arrival (TDOA) or Global Positioning System (GPS)type multilateration. For example, assume that the antenna elements ofeach of these different individual arrays have been calibrated in themanner described above. Here, the locations of three antenna elements ofmodular antenna array 200-1—e.g., antenna elements 200-1 a, 200-1 b, and200-1 d—may be known. The calibration function/device/system 350 maycause each of these three antenna elements to transmit a respectivesignal to enable determination of the location of three antennaelements—e.g., antenna elements 200-N1, 200-N2, and 200-N3—of modularantenna array 200-N, which enables calculation of an offset of a planeof the modular antenna array 200-N relative to a plane of modularantenna array 200-1 that can be used to facilitate coherent beamformingand beamsteering between the modular antenna arrays 200-1 and 200-N. Insome embodiments, the calibration function/device/system 350 may alsocause a fourth antenna element—e.g., antenna element 200-1 c—of modularantenna array 200-1 to transmit a signal, which may enable determinationof a time offset for time synchronization purposes.

In exemplary embodiments, the calibration function/device/system 350 maycalibrate the antenna elements of the modular antenna array 200-1 withthe antenna elements of modular antenna array 200-N. Here, thecalibration function/device/system 350 may determine inter-elementpropagation delays between the antenna elements of the modular antennaarray 200-1 (e.g., each antenna element of the modular antenna array200-1) and the antenna elements of the modular antenna array 200-N(e.g., each antenna element of the modular antenna array 200-N) based onthe determined location(s) of one or more of the three antennaelements—e.g., antenna elements 200-N1, 200-N2, and 200-N3—of modularantenna array 200-N and/or based on known distances between each antennaelement of modular antenna array 200-N and every other antenna elementof modular antenna array 200-N. Subsequently, the calibrationfunction/device/system 350 may employ a process similar to thatdescribed above with respect to the modular antenna array 200-1, such asidentifying a reference antenna element and transmitting Tx and/or Rxreference signals to determine UL and DL offsets. For instance, thecalibration function/device/system 350 may control the digital Txassociated with reference antenna element 200-la to transmit an Rxreference signal, where each antenna element of the modular antennaelement 200-N (e.g., the antenna elements 200-N1, 200-N2, 200-N3, etc.)may receive the Rx reference signal, and where the calibrationfunction/device/system 350 may similarly determine respective Rx (UL)delays or phase offsets associated with the antenna elements of themodular antenna array 200-N relative to the reference antenna element200-1 a. Additionally, the calibration function/device/system 350 maycause each antenna element of the modular antenna array 200-N totransmit a Tx reference signal (e.g., one at a time), where thereference antenna element 200-1 a may receive each of the Tx referencesignals, and where the calibration function/device/system 350 maysimilarly determine respective Tx (DL) delays or phase offsetsassociated with the antenna elements of the modular antenna array 200-Nrelative to the reference antenna element 200-1 a. The calibrationfunction/device/system 350 may then utilize the various phase offsets tocalibrate the modular antenna array 200-N with the modular antenna array200-1.

In various embodiments, the calibration function/device/system 350 mayaccount for waveguide and line of sight (LOS) propagation in determiningUL and/or DL phase offsets and/or in geolocating antenna elementsbetween antenna panels. Waveguide propagation may occur in a case wheretwo modular antenna panels are coplanar and in which a transmission froman antenna element of one of the panels, such as the antenna element200-1 a of modular antenna array 200-1, may, prior to being received byan antenna element of the other panel, such as the antenna element200-N2 of the modular antenna array 200-N, propagate along a portion ofthe modular antenna array 200-1 and/or a portion of the modular antennaarray 200-N as a surface wave. Because the speed of waveguidepropagation is slower relative to LOS propagation, any component of atotal propagation delay that is attributable to waveguide propagationmay need to be taken into account. In certain embodiments, a waveguidepropagation delay may be determined based on properties of material(s)of the various antenna panels. In one or more embodiments, a waveguidepropagation delay can be determined experimentally. In such embodiments,for example, two antenna panels, such as the modular antenna arrays200-1 and 200-N may be positioned flush with one another, side-by-side,such that the arrays are coplanar. Here, a transmission from an antennaelement of one of the panels, such as the antenna element 200-la ofmodular antenna array 200-1, may encounter (e.g., mainly) waveguidepropagation delay and not (e.g., minimal) LOS propagation delay, priorto being received by an antenna element of the other panel, such as theantenna element 200-N2 of the modular antenna array 200-N, in which casea particular waveguide propagation delay may be determined. The twopanels may then be positioned apart from another, and a LOS propagationdelay may be inferred based on a difference between a total measureddelay for a similar transmission and the particular waveguidepropagation delay. A similar process may be performed for one or moreother antenna elements of the modular antenna array 200-N in theabove-described determination of UL/DL phase offsets and/or geolocatingof antenna elements between antenna panels.

It is to be appreciated and understood that the above-described processfor calibration/recalibration between modular antenna array 200-1 andmodular antenna array 200-N can be applied across all of the arrays ofthe multi-array configuration (e.g., where each array may becomecalibrated with each other array of the multi-array configuration 201).Additionally, in various embodiments, some or all of the above-describedcalibration steps/processes may be performed periodically (e.g., dailyor the like) to account for any phase bias drifts. In certainembodiments, the calibration function/device/system 350 may calibratethe various UL delays and DL delays (across the multiple arrays) withone another, such that the relative delays between the UL and the DL arezero (or near zero), so as to overall calibrate the UL with the DL forthe multi-array configuration 201.

Performing calibration as described herein thus enables the multi-arrayconfiguration 201 to function as a coherent antenna system that iscapable of providing collective beamforming and beamsteering (including,for example, for null patterns) among all of the antenna elements of themulti-array configuration 201.

It is to be appreciated and understood that various aspects of theabove-described calibration may be performed at any suitable time frommanufacture of the modular antenna arrays to post-installation or-deployment. For example, calibration of antenna elements of anindividual modular antenna array may be performed upon manufacture(e.g., at a factory) or offline (e.g., while the modular antenna arrayis not in use) after installation or deployment. As another example,calibration of antenna elements across multiple modular antenna arrays(e.g., for coherency) may be performed offline.

In certain embodiments, a least mean squares (LMS) filter can beemployed to enable offline or even online optimization of antennaweights—e.g., for high signal-to-interference-plus-noise ratio (SINR)UEs (e.g., for Rx and/or Tx). The LMS filter may adjust the phases tomaximize the SINR at a calibration UE location, and may store the phasesas calibration phases separate from phases due to the UE channel. Thisapproach can be most effective when the calibration errors are notrelatively large, which might cause an extended LMS filter convergenceissue or even non-convergence.

In various embodiments, calibration data, such as phase offsets (e.g.,respective UL/DL offsets), time delays, amplitudes, etc., can be storedin the device/system 350 or other central repository, for each antennaelement of each modular antenna array of a multi-array configuration,for use in UL and DL operations (e.g., by the corresponding digital Txand digital Rx of each antenna element).

In some embodiments, delays can be translated to a slope across thefrequency band, e.g., phase shift=exp(−j2πfτ) where 2π τ is the slopeacross frequency or rad./Hz. For example, for a 2 nanosecond (ns) delay,this is equal to a slope of 2×10⁻⁹ seconds, with rad./Hz=(1cycle/2π)/(cycle/sec.)=rad.-sec./(2π). Additionally, with 2π*2×10⁻⁹rad./Hz=>over 20 MHz, the phase change is 4π10⁻⁹*2×10⁷=8π×10⁻² rad=>0.25rad. (14 deg.). In certain embodiments, the compensation slope acrossfrequency for a given antenna element can be alternatively applied tothe frequency domain weights to account for differences between the ULand the DL (e.g., to achieve reciprocity for TDD and also across FDDfrequency differences). Further, to measure differences as small as 1nanosecond (ns) or less, in one or more embodiments, the calibrationsignals (which may only be 20 MHz in bandwidth or 50 ns), can beaveraged to facilitate cross correlation measurements down to 10⁻² of asymbol (e.g., with 40 decibel (dB) SINR). Alternatively, in someimplementations, a 100 MHz calibration signal can be generated (e.g.,with 10 ns resolution) and averaged to 10⁻¹, with a SINR of 20 dB orbetter.

In exemplary embodiments, UEs of a cell may need to transmit pilotsignals (e.g., sounding reference signals (SRS)) over the UL, andpossibly spanning across an entirety of a desired communication (ortransmission) channel bandwidth. This enables the system 162 a (e.g.,the vDUs 166 a and/or the vCUs 174 a) to properly estimate the channelbetween a given UE (and its antenna ports) and the base station (or moreparticularly, between that UE and aggregated modular antenna arrays200), which can facilitate various actions, including, for example,tracking of coherence blocks of the UE to determine whether the UE iseligible for Mu-MIMO, deriving appropriate precoding vector(s) for theDL (including, for example, for null forming, especially for a UE inMu-MIMO mode, where nulls may need to be formed and steered in thedirection(s) of other UEs when layer data is transmitted to the UE),determining appropriate combining vector(s) for the UL (including, forexample, for null forming, especially for a UE in Mu-MIMO mode, wherenulls may need to be formed and steered in the direction(s) of other UEswhen data is received from the UE), controlling inter-cell interference,and/or the like. In Mu-MIMO, adequate CSI may be needed toprovide/maintain sufficient network performance for a large number ofusers. Therefore, regardless of whether Mu-MIMO is employed in a higherband (e.g., the C-band) or a lower band (e.g., the Mid-band) andregardless of whether it is used in FDD or TDD, monitoring SRS data ofUEs may be important for purposes of Mu-MIMO management.

While Mu-MIMO provides expanded capacity, UL coverage for a UE may beuniversally reduced with respect to DL coverage (e.g., due to large Txpower differences (e.g., about three orders of magnitude) between thebase station and the UE), and this may be especially troublesome for UEsthat are located farther away from the tower top or the aggregatedmodular antenna arrays 200, or in environments where UL interference canlimit the UL SINR. This can result in SRS sounding deficits, and such aproblem can be exacerbated in cases where a larger desired/availablecommunication (or channel) bandwidth is employed for Mu-MIMO (e.g., 100MHz or the like rather than 10 MHz or the like), and where a UE may berequired (e.g., due poor UL conditions) to transmit pilot signals thatspan an entirety of that channel bandwidth, since the power of each ofnumerous tones that span that channel bandwidth may need to besignificantly reduced in order to accommodate the transmission of all ofthose tones, thereby further reducing UL coverage.

FIG. 4A is a diagram illustrating exemplary, non-limiting coherenceblock-related graphs 410 and 420 that may inform adjustments that can bemade to address SRS sounding deficits in coverage (e.g., for Mu-MIMO) inaccordance with various aspects described herein. In exemplaryembodiments, coherence block information can be utilized to enhanceSRS-based channel estimation, control inter-cell interference (e.g.,pilot signal distribution/control), and/or maximize UL coverage (e.g.,for SRS). A coherence block can be a measure of how long and how much achannel spectrum stays constant. Frequency coherence (or coherencebandwidth) can represent a frequency band across which relativeamplitudes/phases of signals at different frequencies within thefrequency band are consistent. Time coherence (or coherence time) canrepresent a duration over which amplitudes/phases of received signalsare consistent. In exemplary embodiments, a coherence block may be equalto the number of SRS symbols/samples with amplitudes and/or phases thatare coherent across frequency and over time.

Since a coherence block is the product of the coherence time and thecoherence bandwidth (e.g., coherence time multiplied by coherencebandwidth), a relatively large coherence block for a UE can indicatechannel stability, which can reduce a need to probe the channel across alarge portion, or entirety, of a channel bandwidth BW_(tot) 415 in asingle instant of time. That is, less SRS sampling may be sufficient forestimation of the UL channel for the UE. For example, rather thantransmitting SRS (e.g., numerous tones) across a large region ofinterest (larger reporting bandwidth, such as that spanning 20 MHz, forexample), the UE may be permitted to report SRS to the system 162 a(e.g., the vDUs 166 a and/or the vCUs 174 a) for a smaller region ofinterest (smaller reporting bandwidth, such as that spanning 100 kHz,for example). Continuing the example, and additionally, oralternatively, rather than transmitting SRS more frequently (e.g., every2 subframes), the UE may be permitted to report SRS to the system 162 aless frequently (e.g., every frame), or the system 162 a can use thetime coherence of the channel to allow averaging of the SRS data tocompensate for lower SRS SINRs at or proximate to the cell edge (e.g.,within a threshold distance from the cell edge).

As depicted in graph 410, a smaller SRS bandwidth, e.g., F_(rep2), mayapply in a case where the coherence block is larger (e.g., for a line ofsight (LOS) user or UE), whereas a larger SRS bandwidth, e.g., F_(rep1),may be needed in a case where the coherence block is smaller (e.g., anon-line of sight (NLOS) user or UE). As shown, a UE with LOS may beable to tolerate a larger path loss (at or proximate to the cell edge,such as within a threshold distance from the cell edge) than a UE withNLOS, where the full channel bandwidth BW_(tot) 415 may be available forthe UE with LOS for farther distances from the system 162 a (e.g., fromthe RUs 168 a or the modular antenna arrays 200). This may be the caseat least until the distance is large enough that a minimum UL SINR is nolonger satisfied, in which case the channel bandwidth may need to bereduced for the UE with LOS to maintain coverage. Similar principlesapply to time coherence, as shown in graph 420—e.g., where a stationaryUE may be able to tolerate a larger path loss (at or proximate to thecell edge) than a UE in motion, and thus a longer period between SRStransmissions may be sufficient for a stationary UE to maintain ULcoverage.

In exemplary embodiments, the system 162 a (e.g., the vDUs 166 a, thevCUs 174 a, and/or the RIC, such as the RIC portion 164 a-1 and/or theRIC portion 164 a-2 of FIG. 1B) may determine, for UEs, adjustments to(e.g., increases or decreases in) the transmission bandwidth, the SRSbandwidth, and/or a periodicity of SRS based on their coherence blocks.For example, in a case where UL coverage is maintained, decreasing theSRS bandwidth and/or a periodicity of SRS can conserve power for a UEand improve UL coverage. A UE with a larger coherence block (e.g., wherethe coherence block satisfies (e.g., is greater than or equal to) athreshold), such as a UE with LOS or near LOS, a stationary or nearstationary UE, or the like (which may have buffers that are continuouslyfull or near full or where (e.g., historical) throughput is high orexceeds a threshold), can thus benefit from a decrease in transmissionbandwidth, SRS bandwidth, and/or periodicity of SRS, since this wouldreduce or eliminate a need for the UE to transmit over a larger channelbandwidth (or, for SRS purposes, reduce or eliminate a need for the UEto frequently transmit SRS for numerous tones/subcarriers across whatmight otherwise be a fairly stable channel). This conserves powerresources of the UE, which allows for more tolerable path loss and thusincreased overall UL coverage.

For any given path loss, a stationary UE or a UE with LOS or near LOSmay need to expend additional power resources for data transmissionsand/or for SRS reporting purposes. Thus, in a case where UL coverage isdecreasing (e.g., where a stationary or LOS UE is at or proximate to thecell edge, as shown in FIG. 4A), the UE may increase transmit power andtransmit over a smaller channel bandwidth. Where such a UE increasestransmit power to the point where the channel bandwidth is decreased toa minimum channel bandwidth (e.g., a minimum channel bandwidth requiredby a base station to maintain the UL, which may be as low as twophysical resource blocks (PRBs) wide or the like), the ability to probethe full channel bandwidth may be limited, which can hinder the abilityto employ Mu-MIMO. However, in exemplary embodiments, the UE's coherenceblock may be large (e.g., the coherence bandwidth may be relativelylarge compared to that minimum bandwidth and/or the coherence time maybe long, such as on the order of a tenth of a second long, a secondlong, or longer), and thus can be exploited to maintain UL coverage andthereby enable Mu-MIMO. Here, SRS data may not be needed for an entiretyof the channel bandwidth, and so the SRS provided across the smaller SRSbandwidth may be sufficient for SRS purposes given the large coherenceblock (e.g., long coherence bandwidths), thus permitting the UL channelto still be probed. In various embodiments, the system 162 a mayinstruct such a UE to transmit SRS for one or more different, narrowfrequency bands, which increases tolerable path loss (as compared to thefull bandwidth) and thus expands the UL coverage significantly, recoversSRS coverage, and enables Mu-MIMO. In one or more embodiments, thesystem 162 a may determine an average across coherence block(s) for sucha UE (e.g., at or proximate to the cell edge) to obtain an UL channelestimate for the UE. While each of the SRS symbols/samples might beafflicted with noise given the greater distance between the UE and thebase station (or more particularly, between that UE and aggregatedmodular antenna arrays 200), the noise may have zero (or near zero) meanand may have a finite variance, and thus averaging the SRSsymbols/samples (or, in other words, gathering signal energy overcoherent periods) yields a quality UL channel estimate for the UE. Inexemplary embodiments, therefore, the system 162 a may track thecoherence block for a given UE, and exploit a large coherence block(e.g., a coherence block that satisfies a threshold) to increasetolerable path loss, and thus maximize UL coverage for the UE (e.g., byaveraging the SRS over large coherence block(s)).

Accordingly, it is to be appreciated and understood that embodimentsdescribed herein enable restoration or recovery of the above-mentionedSRS sounding deficit in coverage, enabling efficient selectivedeployment of Mu-MIMO. For example, Mu-MIMO may be employed for UEs thathave LOS or near LOS or that are stationary or near stationary (that is,multiple parallel transmissions may be facilitated, where different UEsin Mu-MIMO mode may share physical resource blocks (PRBs)), and Su-MIMOmay be employed for UEs that have NLOS or that are in motion, where allof the UEs of a cell may be serviced via a scheduler using time slots,such as individual time slots for Su-MIMO transmissions and Mu-MIMOtransmissions. Obtaining (e.g., via an interface, such as an O-RANinterface or the like), or estimating, coherence times and coherencebandwidths for UEs may thus prove useful for providing improved servicequality and end-user experience/maximization of UL coverage/capacityoptimization (by employing Mu-MIMO).

In various embodiments, the system 162 a (e.g., the vDUs 166 a and/orthe vCUs 174 a) may track coherence blocks for all served UEs. Acoherence block may represent a number of coherent SRS symbols or anSRS/pilot sequence length, where a longer SRS/pilot sequence lengthincreases the number of available, orthogonal SRS/pilot sequences thatmay be used by UEs (e.g., all UEs served by the cell), as well asneighbor or adjacent cell UEs, for SRS purposes. In various embodiments,the system 162 a may determine the SRS/pilot sequence length anddetermine/generate orthogonal SRS/pilot sequences (e.g.,Zadoff-Chu-based sequences or the like) based on the SRS/pilot sequencelength. For example, where multiple UEs share a coherence block (thatis, the UEs' SRS are consistent across a particular frequency band overa certain amount of time), a size of the coherence block can determinean SRS/pilot sequence length and/or a quantity of orthogonal SRS/pilotsequences.

Because reuse of SRS/pilot sequences in adjacent cells can result inpilot contamination, in exemplary embodiments, the system 162 a (e.g.,the vDUs 166 a and/or the vCUs 174 a) may designate a first subset orgroup of the SRS/pilot sequences for use in its own cell, and distributea different subset or group of the remaining orthogonal SRS/pilotsequences to each of N_(reuse) cells for use in those N_(reuse) cells,where ‘N’ is a number of surrounding/neighboring cells across whichorthogonal SRS/pilot sequences are not to be reused. In this way, nosingle SRS/pilot sequence of the generated orthogonal SRS/pilotsequences may be reused across the N_(reuse) cells. This allows thesystem 162 a to also perform channel estimation for UEs of a neighboringcell (e.g., based upon receiving and identifying transmissions oforthogonal SRS/pilot sequence(s) that have been distributed for use inthat neighboring cell), and enables the system 162 a to generateappropriate nulling for both the UL (combining weights) and the DL(precoding weights) for those UEs of the neighboring cell. In someembodiments, the system 162 a may notify one or more (e.g., each) of thesurrounding/neighboring cells of the first subset or group of SRS/pilotsequences, which can enable those surrounding/neighboring cells tosimilarly identify any UEs transmitting those SRS/pilot sequences asbeing served by the cell associated with the system 162 a, and tosimilarly generate appropriate nulls patterns (e.g., in the UL and theDL) for those UEs. In certain embodiments, a surrounding/neighboringcell may, by virtue of a UE using an unrecognized SRS/pilot sequence oran already used SRS/pilot sequence, infer that that UE is being servedby a different cell, and can similarly generate appropriate nullpatterns (e.g., in the UL and the DL) for such a UE based on theinference.

Selecting an appropriate SRS/pilot sequence length and/or the value ‘N’may, therefore, be useful for maintaining orthogonality in SRStransmissions amongst UEs in a cell and across neighboring cells, whichhelps avoid pilot contamination, ensures proper channel estimation, andfacilitates rejection of intra-cell and inter-cell interference, therebyextending UL coverage and enabling higher network throughput. In someembodiments, the system 162 a may determine an SRS/pilot sequence lengthbased on a smallest-detected coherence block, based on a number of UEsbeing served in the current cell, and/or based on a number of UEs beingserved in one or more neighboring cells to ensure pilot sequenceorthogonality.

In various embodiments, the system 162 a (e.g., the vDUs 166 a and/orthe vCUs 174 a) may be configured to determine an impact zone/regionaround the cell associated with the system 162 a in which UEs served byone or more other neighboring cells are likely to act as a source ofinterference to the UL of the system 162 a or are likely to be subjectedto interference in the DL of the system 162 a. In some embodiments, thesystem 162 a may communicate with other systems 162 a (or base stations)associated with those neighboring cell(s) to identify locations of UEsserved by those neighboring cell(s) as part of identifying UEs that arelocated within the impact zone/region. In this way, the system 162 a canaccount for such UEs when determining a suitable number of orthogonalSRS/pilot sequences to derive and use.

FIG. 4B is a diagram illustrating the benefits of using orthogonal pilotsequences in accordance with various aspects described herein. Here, asshown by reference number 425, a UE1 connected to a first cell mayutilize the same code as a UE2 connected to a neighboring cell, whichmay introduce interference at the neighboring cell. For proper rejectionof UL interference from a UE that is served by a neighboring cell(and/or to prevent interfering with such a UE on the DL), the UL channelassociated with that UE may need to be known. Accurate estimation of theUL channel of such a UE may require the use of orthogonal pilot signals.By using orthogonal codes, as shown by reference number 426, theneighboring cell may be capable of determining and forming nullpatterns/beams in a direction of the UE1 to facilitate interferencerejection. As described above with respect to FIG. 4A, achievingorthogonality may require a sufficient number of codes (e.g., SRS/pilotsequences) to be distributed among neighbor cells, where the number oforthogonal codes may be directly proportional (e.g., nearly equal) tothe length of a given coherence block. In exemplary embodiments, aminimum coherence block, radio resource control (RRC) connections,and/or the number of UEs proximate to the cell edge (e.g., at orproximate to the cell edge) may be used (e.g., in an algorithm) todetermine or compute the available and needed codes. In variousembodiments, a minimum coherence block may be the smallest coherenceblock of all UEs in the cell or all UEs in the cell and neighbor cellclusters that are intended to be included in Mu-MIMO scheduling. In oneor more embodiments, a minimum coherence block may be the smallestcoherence block of all UEs in the cell that are determined to beeligible for Mu-MIMO or parallel transmissions—i.e., transmissions atthe same time and/or over a particular frequency band/allocation (e.g.,a 5 MHz, 10 MHz, 20 MHz, or the like band) or over a certain number ofPRBs or resource elements. In various embodiments, the system 162 a(e.g., the vDUs 166 a and/or the vCUs 174 a) may determine requiredrejection performance, which may involve eliminating UEs (e.g., at orproximate to the cell edge (e.g., within a threshold distance from thecell edge), are traveling at high speeds (e.g., higher than a thresholdspeed), have severe delay spreads (e.g., greater than a threshold delayspread), and/or the like) that have relatively small coherence blocksfrom consideration for parallel transmissions in order to achieveorthogonal code distribution. AI/ML approaches may be employed inexemplary embodiments to assist in this complex scheduling and pilotdistribution task.

In exemplary embodiments, the system 162 a (e.g., the vDUs 166 a and/orthe vCUs 174 a) may (e.g., using one or more AI-based algorithms or thelike) be capable of monitoring for or detecting external sources andperforming action(s) to mitigate interference to and/or from suchexternal sources. For example, as shown in FIG. 4C, there may generallybe external radiating/noise sources (e.g., fixed interferers) located inor near a cell, such as Earth stations (whose locations may be known),repeaters (whose locations may or may not be known), and/or the like,that might affect UL and/or DL performance or that might be affected byout-of-band emissions of the DL. In various embodiments, the system 162a may be configured to monitor for or detect level(s) of interference(e.g., in the UL) from such an external source, and perform one or moremitigative actions. In some embodiments, the system 162 a may utilizeavailable geolocation information (if available) regarding an externalnoise source and/or UL-received signal (e.g., post pilot removal)covariance measurements to determine adjustments for various antennaelements 202 of an aggregation of modular antenna arrays 200. Forexample, UL-received signals may include transmissions from various UEsas well as from external sources. Here, channel vectors associated withthe UEs may be accounted for across the antenna elements 202 of theaggregation of modular antenna arrays 200 (e.g., as described in moredetail below with respect to FIG. 6), where remaining covariance (e.g.,as identified by eigenvalues) may inform on transmissions/interferencefrom the external source(s). In some embodiments, the system 162 a mayutilize (e.g., only utilize) UL-received signals that do not include, orthat are not over, SRS transmissions, which may facilitate identifyingof covariance due to external source(s). In one or more embodiments,adjustments for the various antenna elements 202 may include precodingor the like for null patterns (e.g., quiescent nulls) and/or changes inbeam directions, such that DL transmissions (which may, for example,include parallel transmissions in Mu-MIMO mode) may be steered away from(or avoid) the external noise source(s) and/or may null out-of-bandemissions that might otherwise interfere with the external entity orentities. In certain embodiments, the system 162 a may employ one ormore ML algorithms to learn the spectral signatures of transmissionsemitted by external noise sources (such as frequency and timesignatures, angles of arrival, timing advances, etc.), and may utilizethe learnings to facilitate identifying of such transmissions and/ordetermining of the above-described adjustments relating tosteering/nulling.

In certain embodiments, an antenna system may be configured withadaptive transmit filters that limit DL transmit power. In one or moreembodiments, the transmit filters may include one or more antennapatterns that are programmed (e.g., spatially and/or in frequency) toadjust a transmit filter shape to constrain transmit power, such as thatfor out-of-band emissions. In some embodiments, the system 162 a (e.g.,the vDUs 166 a and/or the vCUs 174 a) may be configured to control theadjusting based on one or more criteria being satisfied, such as basedupon detecting a presence of external noise sources—e.g., Earthstations, repeaters, and/or the like. Enabling adaptive filtering forsuppressing out-of-band emissions reduces or eliminates a need to alteror replace antennas/elements on a tower top.

In exemplary embodiments, the system 162 a (e.g., the vDUs 166 a and/orthe vCUs 174 a) may be capable of performing (e.g., using one or moreAI-based algorithms or the like) passive intermodulation(PIM)/interference cancellation to improve UL coverage. PIM interferencemay be due to nonlinearities that might be external to antennas, such asloose nuts/bolts, rusty appliances, etc., where such external sources,when subjected to electromagnetic waves emitted by antenna elements ofone or more modular antenna arrays 200 in the DL, may generatereflections at frequencies in the UL band, which may negatively impactthe UL and thus reduce the UL coverage. In certain embodiments, thesystem 162 a may cause one or more devices (e.g., one or moreaggregations of modular antenna arrays 200) to perform a beam sweep over(e.g., all) orthogonal beam directions, and may measure UL PIM responsesfor each beam (e.g., as shown by reference number 435 of FIG. 4D). Inone or more embodiments, the system 162 a may utilize available UL PIMresponses to identify a direction/location of external PIM source(s) andperform one or more mitigative actions. Such action(s) may include, forexample, as shown by reference number 436 of FIG. 4D, refraining fromtransmitting beams in the direction of the identified external PIMsources and/or determining or calculating beam patterns (e.g., quiescentnulls 437), to be transmitted by one or more modular antenna arrays 200,for nulling the UL PIM responses. This can avoid DL transmissions fromradiating the PIM source, which reduces or eliminates undesiredreflections thereof off of the PIM source that can otherwise result inUL interference. In various embodiments, a null pattern may be formedinto a quiescent beam pattern that can be concatenated with variousadaptive beams. In this way, PIM/interference cancellation can beperformed to improve/increase overall UL coverage.

In some cases, feedback provided by a UE (e.g., a channel qualityindex/indicator (CQI)) may be used by a base station to adjust DLtransmissions, including, for example, to adjust weights of variousantennas for beamforming and beamsteering. In exemplary embodiments, UEfeedback can rather be used to adjust UL weights of various antennaelements of one or more modular antenna arrays 200 to optimizeprocessing of UL beams. Doing so may be particularly advantageous for aUE that is associated with a small coherence block (e.g., a coherenceblock that is smaller than a threshold), where there may be more channelestimation (SRS) errors. FIG. 4E is a diagram illustrating an exemplary,non-limiting system 440 for beam optimization in accordance with variousaspects described herein. As shown in FIG. 4E, one or more modularantenna array(s) (e.g., aggregated modular antenna arrays 200) mayreceive a beam/signal 442 in the UL from a UE. This beam may be receivedin response to a request (for feedback) transmitted by the system 162 a(e.g., via aggregated modular antenna arrays 200). One or moredemodulators (e.g., orthogonal frequency division multiplexing (OFDM)demodulators or the like) may perform (444) demodulation on the receivedsignal. Demodulator constellation error(s) may be determined bymeasuring an error vector magnitude (EVM) (e.g., the root mean square(RMS) of error vectors). At 446, an error gradient weight adaptation maybe performed to adjust/revise one or more weights for one or moreantenna elements of the modular antenna array(s) 200 based on the EVM.In this way, UL beam processing may be adjusted or optimized and/orchannel estimation errors may be compensated for by fine tuning beamweights.

In certain embodiments, system 440 may additionally, or alternatively,be employed for calibration/recalibration purposes.Calibration/recalibration using system 440 may be performed in addition,or as an alternative, to the calibration process(es) described abovewith respect to FIG. 3. It is to be appreciated and understood thatcalibration/recalibration using system 440 may be performed offline(e.g., while the subject modular antenna array(s) are not in use) orduring operations. In exemplary embodiments, calibration/recalibrationmay include measuring EVM(s) associated with signals received from UEsdetermined to be located proximate to a boresight of the modular antennaarray(s) (e.g., signals having a high SINR, such as those that exceed athreshold SINR level), and performing an error gradient weightadaptation (at 446) to adjust one or more weights for one or moreantenna elements of the modular antenna array(s) based on the EVM. Wheredetermined weight adaptations for high SINR signals associated withmultiple UEs (e.g., a number of UEs greater than a threshold) are thesame or similar (e.g., where the difference between respective weightadaptations is less than a threshold), it can be assumed, for example,that such weight adaptations (or combinations thereof, such asaverage(s) thereof) would likely be effective as calibrationcorrection(s), and the weight adaptations (or combinations thereof, suchas average(s) thereof) can be stored and/or applied for use with thecorresponding antenna elements of the modular antenna array(s). In someembodiments, a determination of whether an interferer is stationary canbe made, where, if so, the antenna weights may be stored until the nextscheduling opportunity arises and may be (e.g., continually) updated forthe interferer with additional scheduling slots.

It is to be appreciated and understood that measures of EVM can beapplied to various processes relating to MIMO described herein,including, for example, processing relating to FDD DL MIMO, propagationdelay determinations, processing delay determinations, MIMO-relatedscheduling, etc.

While for purposes of simplicity of explanation, certain processes areshown/described as a series of steps/blocks in FIG. 4E, it is to beunderstood and appreciated that the claimed subject matter is notlimited by the order of the steps/blocks, as some steps/blocks may occurin different orders and/or concurrently with other steps/blocks fromwhat is depicted/described herein. Moreover, not all illustratedsteps/blocks may be required to implement the methods described herein.

FIG. 5A depicts an exemplary, non-limiting example 500 of multiple UEsbeing served in one or more cells in accordance with various aspectsdescribed herein. As shown, a system/RAN, such as the system 162 a,which may include one or more RUs 168 a (e.g., combination(s) ofcoherent modular antenna arrays 200), may serve multiple UEs 501-505(e.g., corresponding to UEs 170 a) in one or more cells. It is to beappreciated and understood that example 500 can include variousquantities of cells (e.g., Pcells and/or Scells), various quantities ofnetwork nodes in a cell, various types of network nodes and/or cells(e.g., heterogeneous cells, etc.), and/or various quantities/types ofUEs. In exemplary embodiments, the system 162 a can be configured tooperate in TDD and/or FDD, and may be configured to serve the variousUEs 501-505 in Su-MIMO mode and/or Mu-MIMO mode. In various embodiments,and whether the system 162 a operates in TDD or FDD, the system 162 amay utilize the combination(s) of coherent modular antenna arrays 200 toserve the UEs 501-505 in different time slots. For example, a schedulerof the system 162 a may schedule transmissions for (e.g., all) Mu-MIMOmode-eligible UEs in one time slot, may schedule a Su-MIMO mode UE inanother time slot, may schedule another Su-MIMO mode UE in yet anothertime slot, and so on. As another example, the scheduler of the system162 a may additionally, or alternatively, schedule transmissions for(e.g., all) Mu-MIMO mode-eligible UEs over certain PRBs or over afrequency band allocation (e.g., a 5 MHz, 10 MHz, 20 MHz, or the likeband).

FIG. 5B depicts an exemplary, non-limiting example of a scalablearchitecture 530 of aggregated (coherent) modular antenna arrays andclustered processing units in accordance with various aspects describedherein. As shown, architecture 530 can include a vDU cluster 166 c (alsoreferred to as vDUs 166 c, and which may be the same as or similar tothe vDUs 166 a of FIG. 1B) configured to perform SRS data processing(e.g., UL channel estimation in TDD and/or FDD) and determination ofweights (in the UL and DL) for antenna elements of various aggregatedmodular antenna arrays 201 a, 201 b (which may each be the same as orsimilar to the modular antenna array 200 of FIG. 2A) mounted on a towertop 535. In various embodiments, the tower top 535 may be sectorized inthree—e.g., where each sector has 120 degrees of coverage, and where theaggregated modular antenna arrays 201 a may be disposed on one sector ofthe tower top 535, the aggregated modular antenna arrays 201 b may bedisposed on another sector of the tower top 535, etc.

As shown by reference number 552, the vDUs 166 c (or vCU 174 a or RICportion(s) 164 a-1, 2) may obtain SRS data/symbols (e.g., includingamplitude and phase information) from each antenna element of eachcoherent array/panel of each aggregation of modular antenna arrays. Inexemplary embodiments, the vDUs 166 c (or vCU 174 a or RIC portion(s)164 a-1, 2) may instruct a given UE, at the outset, to provide, via eachof one or more antennas of the UE, an initial SRS (e.g., a known SRSsymbol or the like) across an entirety of an available communicationbandwidth and at a particular rate. For example, the vDUs 166 c (or vCU174 a or RIC portion(s) 164 a-1, 2) may instruct the UE to transmit theinitial SRS for every N^(th) tone (e.g., every 4^(th) tone or the like)across multiple PRBs 540. A PRB may span a certain number oftones/subcarriers (e.g., 12 subcarriers or the like) each separated by asubcarrier spacing (e.g., 15 kHz wide, 30 kHz wide, or the like). Basedon coherence block information (e.g., as described above with respect toFIG. 4A), the vDUs 166 c (or vCU 174 a or RIC portion(s) 164 a-1, 2) mayperform one or more corresponding actions. In some cases, where the vDUs166 c (or vCU 174 a or RIC portion(s) 164 a-1, 2) determine, fromcoherence block information associated with the UE, that the coherenceblock is large, the vDUs 166 c (or vCU 174 a or RIC portion(s) 164 a-1,2) may instruct the UE to perform certain adjustments, such as totransmit SRS for a smaller region of interest of the bandwidth (e.g.,for every 8^(th) tone across several PRBs), transmit SRS less frequently(e.g., once every two seconds), and/or the like. In various embodiments,for example, the vDUs 166 c (or vCU 174 a or RIC portion(s) 164 a-1, 2)may, based upon identifying a band where the channel for a UE isconstant or near constant (e.g., where differences between signalamplitudes/phases are within threshold(s)), the vDUs 166 c (or vCU 174 aor RIC portion(s) 164 a-1, 2) may provide an orthogonal pilot sequenceto the UE to be used for subsequent SRS transmissions across thecoherence block (which may involve SRS transmissions across time in acase where the coherence block extends across multiple (e.g., OFDM)symbols in time).

As shown by reference number 554, the vDUs 166 c (or vCU 174 a or RICportion(s) 164 a-1, 2) may cluster the obtained SRS data, and processthe SRS data for one or more (e.g., each) of the PRBs 540, which mayinvolve cross-correlating the SRS data with known reference tones. Asdepicted, each array/panel may be associated with a set of PRBs 540(e.g., 16 in a case where an array/panel supports 16 layers). In variousembodiments, processing the SRS data may include performing channelestimation (with respect to amplitude and phase) for each modularantenna array and/or for an entirety of the aggregation 201 a. Thus,where an aggregation of modular antenna arrays, including multiplepanels, has hundreds or even thousands of antenna elements, and where(e.g., each of) the antenna elements capture a channel estimate in acertain frequency band, the vDUs 166 c (or vCU 174 a or RIC portion(s)164 a-1, 2) may estimate an amplitude and phase for each of the antennaelements for that frequency band.

In various embodiments, where the vDUs 166 c (or vCU 174 a or RICportion(s) 164 a-1, 2) obtain amplitude and phase information fordifferent tones within the same PRB, the vDUs 166 c (or vCU 174 a or RICportion(s) 164 a-1, 2) may calculate an average of the amplitudes and anaverage of the phases for that PRB, and assign the average amplitude toone or more (e.g., each) of the tones of that PRB and assign the averagephase to one or more (e.g., each) of the tones of that PRB. In someembodiments, the vDUs 166 c (or vCU 174 a or RIC portion(s) 164 a-1, 2)may apply the same precoder weight for all of the tones of that PRBaccordingly. Averaging the amplitudes and phases for each PRB can resultin one PRB that included sample data and a next PRB that included sampledata, to exhibit large, step-wise jumps or drops in amplitude/phase. Incertain cases, the vDUs 166 c (or vCU 174 a or RIC portion(s) 164 a-1,2) may perform optimal interpolation for amplitude and phase values ofany PRBs that may have been skipped in the SRS sampling (e.g., that maynot have included any amplitude or phase data) based on bandpass (orsimilar filters) filtering of the data after transformation via FastFourier Transform (FFT) of the frequency data. The subsequent samplingcan be made to be optimal (in the sense of recovering (e.g., all of) theoriginal filtered data) via the Nyquist sampling theorem.

As shown by reference number 556, the vDUs 166 c (or vCU 174 a or RICportion(s) 164 a-1, 2) may share multiple matrix inversions (which may,for example, relate to one or more processes described below withrespect to FIG. 6) across all coherent panels and PRBs (e.g., bysplitting up the PRB workload) as a function of PRB, UE, and/or layerdata.

As shown by reference numbers 556 and 558, the vDUs 166 c (or vCU 174 aor RIC portion(s) 164 a-1, 2) may aggregate calculated UL combining beamweights and DL precoder beam weights (e.g., amplitudes and phases, whichmay be unique for each frequency, each PRB, or across multiple PRBs,depending on the coherency bandwidth) for each antenna element of eacharray/panel, and may send the UL and DL beam weights (including, forexample, data regarding beam pattern formation) to the correspondingarray/panel of aggregated modular antenna arrays 201 a, 201 b, etc. Theantenna elements of each array/panel of each aggregation of moduleantenna arrays can then be controlled to form narrow beams to individualUEs, and properly process beams incoming from the individual UEs, basedon the DL and UL weights, respectively. In this way, a distributedprocessing system can be positioned at a central (or hubbing) location,and can accumulate and process partial data from each modular antennaarray. As described in more detail below, in exemplary embodiments, theUL and DL weights for each antenna element may be different.

In some embodiments, the vDUs 166 c (or vCU 174 a or RIC portion(s) 164a-1, 2) may share the generation of the complete multi-panel matrices(e.g., compute scaling may be required) and, after inversion,communicate the segments of the beam weight matrices to eachcorresponding array/panel. In certain embodiments, UL weights may beupdated per slot, and DL TDD data may be updated per 3 dB covariancechange to accommodate changes in a large scale multipath environment.FDD data can exploit changes in a microscopic fading environment (e.g.,over constant large scale fading) to update the statistical information.

It is to be appreciated and understood that the above-describedprocessing of SRS data can be scalable with the addition of morearrays/panels at the tower top 535.

In TDD, antenna elements operate—i.e., transmit (DL) and receive (UL)—atthe same frequency band or channel, but simply at different times (e.g.,different time slots). Because the UL and DL utilize the same frequencyor spectrum, channel estimation for the UL can be applied to the DL inTDD (e.g., assuming that the DL is calibrated with the UL and precoderDL weights are utilized for/within a coherence block determined from ULSRS). FDD allows for exchange of communications in both directionssimultaneously, since the UL and DL operate in different frequency bandsor channels. However, because of the difference in UL and DLfrequencies, channel estimation of the FDD UL cannot simply be appliedto the FDD DL. For example, it cannot safely be assumed that justbecause there is fading in the UL that there is similar fading in theDL. A poor or improper channel estimation of an FDD DL can negativelyimpact transmission bandwidth/speed and render it difficult to deriveappropriate null patterns for neighboring UEs (e.g., in Mu-MIMO mode).

In exemplary embodiments, to compensate for different phase changes thatsignals of different frequencies may experience in a channel,information (e.g., statistical data) regarding FDD UL(s) can be used(e.g., averaged) to predict the FDD DL. In various embodiments, channelstatistics can be extracted from UL-received signals for different UEs(e.g., the UEs being served by the cell), and combined (e.g., averaged)to predict the large scale fading DL channel. Combining or determiningan “expectation” of (e.g., an average of) UL channel statistics canprovide information regarding a general “shape” of the large scalefading channel, and enables determination of the general (e.g., azimuth)direction of neighboring UEs relative to the aggregated modular antennaarrays 200, which can aid in identifying appropriate, narrow nullpatterns for those UEs. For example, in a case where there is fastfading in the FDD UL (e.g., where coherence time of the channel isrelatively small or where amplitude/phase vary considerably over a shortperiod of time), for example, the fast fading can be averaged over apredefined (e.g., reasonable) time and used to derive appropriate,narrow null patterns for neighboring UEs.

In conjunction with the ability to form narrow beams using aggregatedmodular antenna arrays, being able to form narrow null patterns enablesfiner separation of UEs, and thus more efficient and effective Mu-MIMOin FDD and optimized capacity.

As an example, in FDD, assume that the system 162 a determines, based ontracking of coherence blocks of multiple UEs—e.g., UEs 501, 502, and 503of FIG. 5A, that there is a coherence block, for each of these UEs, overa 1 MHz band of a 100 MHz channel bandwidth and over a time period of 1ms. That is, assume that the coherence block, for each of these UEs,spans 1 MHz in frequency and 1 ms in time, such that SRS is constantover this block, and where SRS then changes for the next 1 MHz infrequency and next 1 ms in time, and so on. A quantity of orthogonalpilot/SRS sequences (e.g., Zadoff-Chu sequences) can be determined basedon this coherence block size, which can enable a corresponding quantityof UEs to be multiplexed across each of these coherence blocks, wherethe orthogonal pilot/SRS sequences may be reused, for example, for each1 MHz band across the 100 MHz channel bandwidth. Here, the system 162 amay measure the UL channels for each of the UEs (e.g., UEs 501, 502,503, etc.) by performing cross correlation (where the orthogonalpilot/SRS sequence for that UE may be extracted/removed and crosscorrelation of channel vectors for that UE may yield a complex numberthat represents that UE's channel), and averaging different, independentsamples of that UE's channel as the channel changes every 1 MHz bandand/or as the channel changes every 1 ms in time. This enablesdetermination of appropriate FDD DL weights for the multiple UEs to bemultiplexed in Mu-MIMO mode. In exemplary embodiments, while FDD ULweights for these UEs may be periodically updated (e.g., per slot asbriefly described above), the FDD DL weights for these UEs may (e.g.,may only) be updated based on detecting a change in cross covariance(which may, for example, be due to movement of one or more of these UEsand thus changes in multipath) that satisfies a threshold (e.g., achange in cross covariance that is greater than 3 dB or the like). Invarious embodiments, a change in a general “shape” of a UE's spatialcorrelation data—e.g., which may be determined from recalculating R_(k)described in more detail below with respect to FIG. 6—may trigger FDD DLweight(s) to be updated.

FIG. 6 shows exemplary, non-limiting equations 620, 630, 640, and 650that can be applied to implement Mu-MIMO in accordance with variousaspects described herein. Generally speaking, one or more embodimentsdescribed herein can perform DL channel estimation based on ULperformance indicators, despite the DL and UL channels being operated atdifferent frequencies. DL channel quality estimation can be useful forDL precoding and for preventing interference between multiple beams of aMu-MIMO system. Exemplary embodiments provide an example heuristicalgorithm for DL beamforming, with some implementations being based on agiven UL CSI over a narrow frequency/spatial range. It is to beappreciated and understood that different modifications and expansionsof the algorithm may be made to facilitate use thereof in other contextsor circumstances. The example algorithm may employ an M×M matrixinversion for the DL precoder and Eigenvector calculation, where variousprinciples described herein can also apply to K×K matrices. Here,performance of the example algorithm is quantified for three cellscenarios.

In a first scenario, a single cell may be served by a MIMO base station(e.g., the system 162 a, such as the vDUs 166 a and/or the vCUs 174 a)with an M element MIMO antenna array (e.g., modular antenna array(s) 200or the like) serving K users (e.g., UEs), each having a single Rxantenna. In this example, weight coefficients may be applied to theantenna elements of the MIMO antenna array according to equation 620,where W is the M×K matrix of weight vectors, where each column is thecomplex weight vector to be applied to serve a given UE, where H is theM×K channel matrix, where each of the K columns represents the channelvector whose elements are the complex-valued channel gain between eachMIMO antenna element and the single Rx antenna of the UE, where the [*]denotes a conjugate transpose, where I_(M) is the M×M identity matrix,where σ² is the noise at the UE, and where p=P_(Tx)/K is the totaltransmit power of the base station divided by K (e.g., the number ofUEs). In one approach, the channel vectors that can comprise H may bechannel vectors obtained within a given coherence block acquired fromthe use of orthogonal pilot signals transmitted from each UE at the ULfrequency. In various embodiments, equation 620 (or a similar equation)may be applied for estimating a TDD UL, a TDD DL, and an FDD UL. In someembodiments, complex values may be obtained for each antenna elementbased on received UL signals (e.g., pilot signals, where known (e.g.,orthogonal) SRS sequences may be removed).

In some circumstances, it may be difficult to utilize weights equation620 for FDD DL beamforming since the H matrix in equation 620 describesCSI at the UL frequency, which may be significantly different from thatat the DL frequency. This difference is primarily due to differences inphases acquired when ray paths of the same length are traversed atdifferent wavelengths. This can, for example, affect the fast-fadingcomponent of the CSI. While the H matrix contains contributions fromboth fast-fading components and slow-fading components of the channels,the spatial correlation matrix R, defined for a single channel byequation 630, describes contributions only from slow-fading components,e.g., path loss, shadowing, and spatial correlation.

In equation 630, h_(k) is an M×1 complex channel vector for the k^(th)UE and E{ } denotes an expectation value. It is noted that R is an M×Mcomplex matrix because h h* denotes an outer product (rather than aninner product) of the channel vectors. Thus, as it primarily describesslow-fading characteristics, it is expected that the spatial correlationmatrix R will be much less sensitive to changes in frequency. Theheuristic beamforming approach described in this example can generalizethe regularized zero-forcing method in equation 620 such that it isexpressed in terms of spatial correlation matrices rather than Hmatrices. Though these spatial correlation matrices may be constructedfrom channel vectors acquired at the UL frequency, in one or moreembodiments, by taking the expectation value, which, in practice, mightbe accomplished by averaging h_(k) h_(k)* over many coherence blocks,the resulting spatial correlation matrices can be relatively insensitiveto frequency as compared to other approaches. An example of thisapproach is described by equation 640, with the M×M matrix Q being thesum over spatial correlation matrices for all UEs in the cell, e.g.,depicted in equation 650. In one or more embodiments, the M×K matrix Vappearing in equation 640 can be constructed by taking the M×1dimensional eigenvector of R_(k), with the largest eigenvalue andconcatenating those eigenvectors for each of the K users, to form thecolumns of the matrix V. Before applying them, each of the weightvectors in the columns of the M×K dimensional matrix W can be normalizedsuch that the power transmitted by the base station to each UE isp=P_(Tx)/K. In certain embodiments, the eigenvector with the largesteigenvalue can be simply replaced with the estimated channel vector forthe UE of interest. In various embodiments, equations 630, 640, and/or650 (or similar equations) may be used for predicting an FDD DL. Forexample, equations 630, 640, and/or 650 may be used for predicting anFDD DL for UEs in Mu-MIMO mode scheduled for a particular time slot overa particular set of PRBs, where SRS transmitted by such UEs (and notother UEs that may be in other modes, such as the Su-MIMO mode) may beused to determine the channel vectors. In various embodiments, the knownchannels can be replaced by covariance functions for FDD (and TDD)applications where significant interference is expected.

FIG. 7 depicts an illustrative embodiment of a method 700 for FDDMu-MIMO in accordance with various aspects described herein.

At 702, the process may include tracking, for a UE, a coherence blockand identifying (e.g., estimating or obtaining) a coherence time (T_(c))and a frequency coherence bandwidth (F_(c)). In various embodiments,estimation of T_(c) and/or F_(c) may be fine-tuned for errors (thatmight be imparted to channel estimates) via a feedback mechanism.

At 704, the process may include determining an SRS/pilot sequence lengthbased on T_(c) and F_(c). For example, the SRS/pilot sequence length maybe ≤T_(c)*F_(c). The SRS/pilot sequence length may inform the number ofunique SRS codes that can be deployed to a cluster of cells. In variousembodiments, the values T_(c) and F_(c), for a UE that has the smallestcoherence block (e.g., fewest number of SRS symbols) relative to otherUEs, may be used for determining the SRS/pilot sequence length. In someembodiments, a coherence block threshold may be defined for determiningwhether a UE may be eligible for parallel transmissions or Mu-MIMO. In acase where a UE's coherence block does not satisfy the threshold (e.g.,is less than or equal to the threshold), the UE may be determined to beineligible for parallel transmissions or Mu-MIMO. In such a case, thatUE's coherence block may or may not be used in the determining of theSRS/pilot sequence length.

At 706, the process may include providing an SRS request to the UE. Invarious embodiments, the SRS request may identify an SRS period PI(where, for example, PI<T_(c)), and SRS bandwidth B (where, for example,B<F_(c)), etc.

At 708, the process may include estimating an UL channel for the UE viacross-correlation with a reference SRS for (e.g., each) SRS bandwidth B.In various embodiments, and in a case where PI<<T_(c), an estimate maybe improved via averaging. In some embodiments, such as in a case wherethe UL SINR is poor for a UE (e.g., satisfies (e.g., is less than orequal to) a threshold SINR) and PI˜T_(c), the process may includeimposing UL Tx power control for the UE to obtain an improved estimatein such cases where averaging might not be possible.

At 710, the process may include calculating an UL weight matrix. Inexemplary embodiments, the UL weight matrix may be calculated using theformula: W (M×K)=H^(d) (σ²/P_(ui) I+Q^(d))⁻¹, where H^(u)=UL channelmatrix (M×K), M=number of adjacent antenna elements, K=number of UEs,Q=H*H (K×K), σ=UL noise standard deviation, and P_(ui)=UL UE Rx powerfor the i^(th) UE. In various embodiments, scheduling slots (e.g., eachscheduling slot) may be updated.

At 712, the process may include estimating a DL channel based on the ULchannel cross covariance, Q^(u)=E{Q^(d)}, where it is assumed thatstationary ergodicity can be estimated as a time average. In variousembodiments, Q=(1/L)Σ₁ ^(L)H*H, where H=the UL matrix of M×K channelestimates.

At 714, the process may include calculating a DL weight matrix. Inexemplary embodiments, the DL weight matrix may be calculated using theformula: W (M×K)=H^(d) (σ²/P_(di) I+Q^(u))⁻¹. In exemplary embodiments,P_(di) may be equal to the downlink Tx power allocation to the i^(th) UE(which can, for example, be adjusted based on “priority”). In variousembodiments, the weight matrix may be adjusted for the DL and ULfrequency difference (e.g., interpolated for downstream higher than theuplink SRS estimates). H^(d) can be an averaged estimate, an eigenvectorcalculation from M×M channel outer product E{hh*}, or can be from Type IUE feedback (e.g., at the expense of DL CSI-RS overhead and UL precodingmatrix indicator (PMI) feedback). In one or more embodiments, the DLweight matrix may be adjusted for DL/UL frequency deltas. In certainembodiments, the DL weight matrix may (e.g., may only) be calculated orupdated in cases where the channel covariance changes significantly(e.g., multipath environment changes) rather than in cases ofmicroscopic changes (e.g., fast fading, etc.).

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 7, it isto be understood and appreciated that the claimed subject matter is notlimited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In certain embodiments, coherence Time (T_(c)) may be computed. T_(c)may determine (i) how often FDD DL weights are updated (e.g., an“independent sample” may be added), where weights may be fixed for(<T_(c)), and (ii) a maximum interval that FDD UL SRS can be measured,where UL weights may be fixed for (<T_(c)). T_(c) may determine if SRSpilot signal(s) can have reduced bandwidth (e.g., at or proximate to thecell edge) to satisfy UE maximum transmit (Tx) power constraints, whilestill maintaining coherence time constraints. For example, a long T_(c)can benefit a UE at or proximate to the cell edge (e.g., withoutsufficient Tx power to send the full SRS bandwidth) by allowing the UEto transmit portions of the SRS constant over the coherence bandwidth(F_(c)). In some embodiments, FDD DL calculations may include averagingover coherence time intervals, up to a time where large scale channelparameter(s) deviate (e.g., where the shape of the coherence time orT_(c) changes significantly, such as by 20% or more or the like).

In certain embodiments, coherence bandwidth (F_(c)) may be computed.F_(c) may determine the number of tones/PRBs where the DL weights can bethe same per antenna element (e.g., not including interpolation afterthe weight calculation(s)). F_(c) may also determine how often frequencydomain averaging for FDD can be performed for computing DL weights(e.g., FDD covariance averaging can be performed over frequencybins/tones on the order of the F_(c) until a time where the frequencycovariance changes significantly, such as by 20% or more or the like).F_(c) may additionally determine how wide the SRS needs to encompass,unless F_(c) is about equal to the system bandwidth (e.g., flat fading).

In certain embodiments, a covariance function change may occur when“shapes” of time (e.g., coherence time) and bandwidth (e.g., coherencebandwidth) curves change (e.g., significantly, such as beyond athreshold). FDD DL weights may be adjusted based on such a covariancefunction change. Where the shapes are the same (or similar), thecovariance function can be averaged (e.g., an exponential weightingfunction or a first order recursive average calculation may beperformed).

An adaptive weighting factor may vary as the shape(s) of the function(s)change. For example, assuming a default of 1 meter with user velocitycalculated and time constant: T=1 meter/UE velocity (meters/second(m/s)).

A cluster Blk may be approximately equal to min(Blk per UE). A coherenceblock (Blk samples) may be equal to coherence time (T_(a))*coherencebandwidth (F_(e)), which may be greater than (>) SRS length (e.g.,approximately the number of UEs served by the cell in addition to thenumber of first tier neighbor cell UEs). That is, Blk may determine howmany orthogonal UL SRS sequences can be used per cell cluster (e.g., theserving cell and/or first tier neighbor cell(s)). The number of layers Kmay be less than (<) overhead (OVH)*Blk/N_(reuse)=0.1*Blk/4=0.025*Blk,where Blk>640 for K=16, OVH=10%, and N_(reuse)=4. If Blk=640 andF_(c)˜500 kHz, then T_(c)=640/500 kHz=1.3 milliseconds (ms) (orgreater). If Blk is this long, for example, there may be sufficient SRSZadoff-Chu sequences for about 600 orthogonal SRS users for the cellcluster (e.g., serving cell and first tier neighbor cell(s)). Covariancefunction decorrelation may be the time or frequency when the function is3 dB down from the peak, and this may be where FDD DL weights areupdated (and/or SRS is updated to track microscopic fading). In certainembodiments, OVH (e.g., pilot signal overhead per Blk) may be equal tothe number of pilot samples per coherence block: the number of activeusers per cell (K)*sequence reuse factor (N_(reuse))=K*N_(reuse)/Blk.Keeping OVH<10% might yield K*N_(reuse)/Blk<0.1, where for N_(reuse)=4:K/BLK<0.025. Throughput (T_(put)) may be approximately equal to(1−OVH)*K*DR, where OVH=K*N_(reuse)/Blk, and where optimal K<Blk/2. Insome embodiments, ranges of Blk may be in the area of F_(c)˜500 kHz andT_(c)˜1 to 100 ms—e.g., BLK˜500 to 50,000. Where OVH is constrained to<0.1, for example, then K<0.025*Blk or Ko=12.5 to 1250.

FIG. 7A depicts a table 720 and example equations—EQ 1 and EQ 2—that canbe applied to mitigate various types of input interference in accordancewith various aspects described herein. As depicted, input interferencemay, from the perspective of a jth serving cell, originate from one ormore mobile UEs in a neighbor cell ‘1’ (i.e., lower case L), one or morefixed UEs, one or more external active sources Tx, one or more externalpassive sources Rx, and/or one or more PIM sources. Here, for example,the system 162 a of FIG. 5A (e.g., the jth serving cell) may, as part ofserving UEs 501 to 505, identify such input interference, and apply EQs1 and/or 2 to define nulling for one or more external interferersassociated with neighbor cell ‘1’. EQ 1 may represent a minimum meansquare estimate of a channel H_(li), given an observation yli (which maybe known). Similar to equation 630 of FIG. 6, Rh may represent anexpectation of h h*; that is, R_(li)=E{h_(li)h_(li)}. In EQ 2,Ψ_(li)=E{y_(li)y_(li)}. Generally speaking, table 720 and EQs 1, 2enable calculation of the covariance, where interference from anexternal interferer (which may be independent of noise and signal) maybe identified, such as in a case where the interference is larger thanthe signal. Obtaining an inverse (or reciprocal) thereof enables nullingout of that interference in a general direction of the externalinterferer. In various embodiments, equation 620 of FIG. 6, for example,may apply for a particular (e.g., optimum) scenario where the channelsfor served UEs (e.g., all served UEs) are known and noise is identified.There, UEs (e.g., UEs 501 to 505 of FIG. 5A) may actively transmit SRS(known orthogonal pilot sequences), which may be cross correlated out toobtain a channel model for each of the UEs, where a channel model for agiven one of the UEs may involve multipath signals (arriving atdifferent angles) that may need to be nulled when transmissions arereceived/transmitted for another one of the UEs. Referring back to FIG.7A, after obtaining the autocovariance based on received signals (whichmay include external interference), for a given UE, such as UE 501 ofFIG. 5A, summation (e.g., R) of known information (e.g., known channelsas per equation 620 of FIG. 6) for other served UEs (such as UEs 502 to505 of FIG. 5A) can be subtracted from the autocovariance to identify orisolate the (e.g., general direction of the) external interference.Here, as depicted in table 720 of FIG. 7A, for certain inputinterference, equation 620 of FIG. 6 may be adapted, for TDD, such thatψ (i.e., EQ 2) may be substituted for the term “I_(M)+H H*” in equation620, and equation 640 may be adapted, for FDD, such that ψ (i.e., EQ 2)may be substituted for the term “I_(M)+Q” in equation 640. In variousembodiments, for FDD precoding, a resampling approach may be used toaccount for the angle of arrival. In this way, nulling can be effectedfor UEs 502 to 505 as well as for the external interference.

FIG. 7B is a diagram of example antenna elements of a modular antennaarray in which a phase difference between uplink and downlink signals inFDD may be compensated in accordance with various aspects describedherein. As depicted in FIG. 7B, antenna elements 200 u and 200 v may beincluded in a modular antenna array, such as modular antenna array 200of FIG. 2A. Here, antenna elements 200 u and 200 v may be aligned in anX-direction. It is to be appreciated and understood that, although onlytwo antenna elements 200 u and 200 v and two DL transmissions S_(u) andS_(v) are depicted in FIG. 7B, there may be additional antenna elementsand/or additional DL transmissions. In an extreme case where the antennaelements 200 u and 200 v emit DL transmissions in the Y-direction, aresulting plane wave may constitute a sinusoid in the Y direction and aconstant in the X-direction. In a general case, emitting DLtransmissions S_(u) and S_(v) from the antenna elements 200 u and 200 vat an arbitrary angle θ may result in a phase shift betweentransmissions S_(u) and S_(v), which may be represented as distanceP=d*sin θ=(λ/2)*sin θ, where d (or λ/2) is the distance between theantenna elements 200 u and 200 v. In a specific limiting case, where asine wave is launched in exactly the X-direction, for example, d may beequal to λ/2=180 degrees. To achieve zero phase shift between the DLtransmissions S_(u) and S_(v), in this case, the transmission S_(u) maybe shifted or “advanced” by 180 degrees. In FDD, the DL transmissionfrequency is different from that of UL-received signals. While nulls maybe easily applied in the UL in FDD, nulls that are applied in the DL maybe directed in incorrect or unintended directions. Boresight signals maynot be affected by the difference in UL and DL frequencies in FDD, butend-fire, or near-end-fire, signals (i.e., a signal at an angle ofdeparture, relative to the X-direction, that satisfies (e.g., is lessthan or equal to) a threshold angle) may be most affected. For example,for UL end-fire signals (e.g., in the X-direction), a higher DLfrequency in FDD may not have zero crossings at λ/2 as the UL. That is,assuming a higher DL frequency than the UL frequency in FDD, a DLwaveform launched across the antenna elements may have more cyclesacross the aperture than desired or required. Here, a higher DLfrequency results in a smaller λ value—e.g., a λ′ value that is smallerthan λ—which changes the distance P (or phase difference), and thus theangle of departure from the UL angle of arrival. As an example, a largerDL frequency may yield a larger phase difference, and thus a largerangle of departure. For a given angle θ, therefore, directionality of aresulting DL plane wave can be different than in a case where λ′=λ. Forinstance, for end-fire UL signals, a resulting DL waveform in FDD maynot be launched as an end-fire DL waveform, but rather at a differentundesired angle or direction.

In exemplary embodiments, given the frequency delta in FDD between theUL and the DL, the DL data may be pre-compensated. In particular,received UL data may be extrapolated by a ratio of the frequency of theDL relative to the frequency of the UL. As shown by reference number738, assume that the frequency of the DL is 2.2 GHz and the frequency ofthe UL is 2.0 GHz. Here, the ratio may be 2.2 GHz/2.0 GHz=1.1, and thus,about five percent of additional data may be added or extrapolated toeach end of the received UL data, as depicted, and the total resultingUL data may be matched (e.g., precoded) to what is to be transmitted inthe DL over the original array aperture. This may thus involvegenerating data that exceeds or that is larger than the array aperture,and then compressing that data to fit in the array aperture, where DLdata obtained based on such extrapolated and compressed data (e.g.,obtained from UL channel estimation and predicting of the DL channelbased thereon, as described elsewhere herein) would also fit in thearray aperture. This may adjust or change the relationship between theantenna elements 200 u and 200 v by the above-described ratio, resultingin the phase change or difference between the antenna elements 200 u and200 v to be the same as the phase change or difference in the ULdirection in end-fire and/or near-end-fire cases (i.e., a signal at anangle of departure, relative to the X-direction, that satisfies (e.g.,is less than or equal to) a threshold angle). In various embodiments,the above-described compression may be implemented by applying the ratioas a scalar a of a vector f(t)—e.g., f(αt), where a can be greater than1 or less than 1 and may provide time- or spatial-based scaling of thevector. Obtaining a Fourier Transform of f(αt), where F(f/α) is theFourier Transform pair of f(αt), and a provides time- or spatial-basedscaling, effects a frequency change in the frequency domain. Bytransforming the adjustment from the spatial domain to the beam domain,the direction or angle of the DL beam can thus be adjusted such thatnulls may be pointed in the proper direction. In one or moreembodiments, the system 162 a may apply the above-describedextrapolation and compression to raw UL channel data (e.g., the signalswithin the H H* of equation 620 after SRS has been correlated out) toobtain the desired phase change in the UL channel data, and maysubsequently derive DL data therefrom (e.g., as described above withrespect to equations 630, 640, and 650). In alternate embodiments, thesystem 162 a may first derive DL weight data from the UL data (e.g., asdescribed above with respect to equations 630, 640, and 650), and maysubsequently apply a form of the above-described extrapolation andcompression to the DL weight data to derive DL data with the desiredcompensation in phase change.

In exemplary embodiments, aggregated modular antenna arrays 200 may beemployed for both Su-MIMO and Mu-MIMO (e.g., in different time slots asarranged by a scheduler). The aggregated modular antenna arrays 200 maybe used in TDD or FDD.

Many UEs may operate using legacy technology, and thus may have limitedsupport for CSI-RS. For example, a legacy UE may have a small number ofCSI-RS ports, such as 4 or 8, which may be inadequate for a high T/Rmassive MIMO system, where, for example, aggregated modular antennaarrays 200 may include hundreds of antenna elements or more. In a casewhere each of these antenna elements transmits an orthogonal CSI-RS, theamount of overhead needed for the UE (e.g., per coherence block) and/orthe link could be insurmountable.

In various embodiments, for a UE—e.g., the UE 504 of FIG. 5A—in Su-MIMOmode in TDD, the system 162 a (e.g., the vDUs 166 a and/or the vCUs 174a) may leverage reciprocity for estimating the DL channel. Here, the UE504 may transmit power- and bandwidth controlled SRS (e.g., using eachof multiple antennas of the UE), which the system 162 a may utilize toestimate the UL channel (e.g., by applying equation 620 of FIG. 6 in acase where equation 620 is adapted for determining UL weights, and wherep in equation 620 represents UL power, which may be different for eachUE). The DL can also be estimated by analyzing channel vectorsassociated with the UE 504—e.g., by determining eigenvalues relating toH H* of equation 620 of FIG. 6, which can inform on an appropriateSu-MIMO rank (e.g., number of data layers) to assign/use for the UE 504and how the system 162 a (e.g., the base station) is to be configured.In various embodiments, maximum ratio transmission may be employed,where DL weights may be matched to channel fading as determined from theUL channel estimation. By leveraging UL- or reciprocity-based estimationin a TDD system, a UE in Su-MIMO mode can be spared of having to providefeedback on the DL, such as feedback on CSI-RS. That is, for example, incertain embodiments, CSI-RS-based processing may be eliminated for UEsin Su-MIMO mode in TDD, where channel estimation for both UL and DL maybe (e.g., only) reciprocity-based (e.g., SRS-based).

In various embodiments, for a UE—e.g., the UE 504 of FIG. 5A—in Su-MIMOmode in FDD, the system 162 a (e.g., the vDUs 166 a and/or the vCUs 174a) may similarly estimate the UL channel using SRS. Additionally, in oneor more embodiments, the system 162 a may leverage the channel vectors,determined based on the SRS, to predict the DL channel for the UE 504.For example, the system 162 a may utilize one or more of equations 630,640, and 650 (or versions thereof) to determine DL precoding weights forthe UE 504. Continuing the example, the system 162 a can identifyeigenvalues relating to R_(k), where k represents the UE 504, and canperform an average of the channel vectors for the UE 504 to predict theDL channel for the UE 504. In various embodiments, maximum ratiotransmission may be employed, where DL weights may be matched to channelfading as determined from the UL channel estimation. Here, CSI-RStransmissions and overhead may similarly be reduced or eliminated for aUE in Su-MIMO mode in FDD. Given that Su-MIMO users generally havesmaller coherence blocks, and thus lower available capacity for DLsymbols, minimizing the transmission of CSI-RS data can conserveresources for transmission of actual user data.

In certain embodiments, for a UE—e.g., the UE 504 of FIG. 5A—in Su-MIMOmode in FDD, the system 162 a may additionally or alternatively, provideDL CSI-RS, which the UE 504 may respond to with feedback (e.g., a PMI orquantized feedback weights) that the system 162 a may utilize for DLestimation and beamforming. Such feedback (and/or additionalinformation) may reveal a desired Su-MIMO configuration for the UE 504,such as, for example, a ranking for Su-MIMO relating to a desired numberof layers. In various embodiments, as part of nevertheless minimizingCSI-RS transmission for a large antenna system, such as an aggregationof modular antenna arrays 200, the system 162 a may identify an antennaelement on each “side” of the antenna system to use for CSI-RS. For anaggregation of modular antenna arrays 200 (where the aggregation mayinclude two, three, or more antenna panels), the system 162 a maylogically partition the aggregation (e.g., in half, in quarters, or inany other suitable division, etc.), resulting in partitions from whichantenna elements may be selected for CSI-RS purposes. For instance, thesystem 162 a may logically partition the aggregation of modular antennaarrays 200 in half, in a horizontal direction, resulting in a “left”partition and a “right” partition of the antenna system. Continuing theexample, the system 162 a may identify a first antenna element on theleft partition and a second antenna element on the right partition, andcause the first antenna element to output orthogonal signals in multiplepolarizations (such as two polarizations, e.g., +45 degrees and −45degrees) and the second antenna element to output orthogonal signals indifferent polarizations (such as two polarizations, e.g., +45 degreesand −45 degrees), resulting in multiple (e.g., four) orthogonal CSI-RS,where the multiple T/R element array(s) may “appear” to the UE as just afour T/R or port array. The UE 504 may, based upon receiving theorthogonal CSI-RS, provide one of a variety of (e.g., sixteen) differentquantized feedback weights (or matrices), which the system 162 a can useto determine, or maximize, a ranking for Su-MIMO (e.g., to rank 4 orhigher). Transmitting CSI-RS in this manner—that is, using only a subsetof the antenna elements of an aggregation of modular antennaarrays—reduces DL CSI-RS overhead for a UE in Su-MIMO mode and enablesthe aggregation of modular antenna arrays 200 to transparently serve theUE in Su-MIMO mode (e.g., based on the requested Su-MIMO rank, such asrank 4, etc.) and other UEs—e.g., the UEs 501, 502, and 503—in Mu-MIMOmode.

In various embodiments, the system 162 a may repeat the above-describedprocess (e.g., using the same or different partitions, using some or allof the same antenna elements, or using different antenna elements)periodically, based upon one or more criteria being satisfied, and/orthe like.

It is to be appreciated and understood that the system 162 a canidentify the first antenna element or T/R and the second antenna elementor T/R in any suitable manner, such as randomly, based on a sequence orpattern, by position (e.g., identifying only the antenna elements orT/Rs that are positioned at a corner portion (e.g., a top corner portionand/or a bottom corner portion) of the antenna system), by designation(e.g., always selecting a particular antenna element on the left side ofthe antenna system and/or always selecting another particular antennaelement on the right side of the antenna system), and so on.Furthermore, in various embodiments, the system 162 a can identify agroup of antenna elements (rather than a single antenna element) in eachof different partitions of the aggregation of modular antenna arrays 200for CSI-RS purposes. For example, in a case where the system 162 alogically partitions the aggregation of modular antenna arrays 200 inhalf, in a horizontal direction, resulting in a left partition and aright partition of the antenna system, the system 162 a may identify afirst group of antenna elements (e.g., four antenna elements) on theleft partition and a second group of antenna elements (e.g., fourantenna elements) on the right partition, and cause each antenna elementin the first group of antenna elements to output orthogonal signals inmultiple polarizations (such as two polarizations, e.g., +45 degrees and−45 degrees) and each antenna element in the second group of antennaelements to output orthogonal signals in different polarizations (suchas two polarizations, e.g., +45 degrees and −45 degrees), resulting inmultiple (e.g., sixteen) orthogonal CSI-RS.

In this way, the system 162 a may leverage both reciprocity-based and/orfeedback-based (e.g., CSI-RS-based) estimation for UEs in Su-MIMO mode,with minimal overhead to the UEs even with large aggregations of modularantenna or T/R arrays.

As can be seen, processing associated with various embodiments describedherein, including embodiments relating to antenna element weightcalculations, combining/precoding/beamforming, coherence block tracking,multi-array calibration/recalibration, SRS processing, interferencemitigation, etc. may involve monitoring and controlling of variousparameters of a MIMO system (e.g., a Su-MIMO system and/or a Mu-MIMOsystem), whether in FDD or TDD in any suitable frequency range (e.g.,Frequency Range 1 (FR1), Frequency Range 2 (FR2), and/or the like). Inexemplary embodiments where modular antenna arrays 200 arecommunicatively coupled to DUs/vDUs and/or CUs/vCUs of a RAN (or C-RAN)via open/accessible interfaces, as described above, the interfaces maybe used for the monitoring and controlling of various parameters. Here,AI/ML may be employed by the RAN (e.g., by one or more RICs or RICportions of the RAN/system 162 a, such as the RIC 164 a, the RIC portion164 a-1, and/or the RIC portion 164 a-2) to achieve (e.g.,operator-specific) policies and goals, such as those relating tooptimizing user experience and spectrum capacity (which may involve, forexample, balancing overall system capacity with individual userexperience or balancing bandwidth provided to groups of users (e.g.,stationary or fixed wireless users) with interference from such users toother users (e.g., high-mobility users)). For example, in a case wherethe RAN 162 a of FIG. 1B is in an O-RAN implementation, various RANinterfaces, such as the E2, A1, O1, and FH interfaces may be utilized topermit monitoring (“dials”) and controlling (“knobs”) of parameters of aMIMO system. Continuing the example, the RIC portion 164 a-2 (e.g., anear real-time RIC) and/or an associated E2 interface may be used tofacilitate beamforming functions. Further continuing the example, theRIC portion 164 a-1 (e.g., a non-real-time RIC) may be configured totrain AI/ML model(s) (e.g., an rAPP), relating to MIMO beamforming/enhancements (MBE), using O1 and/or fronthaul (FH) M-planemeasurement data (e.g., from the RUs 168 a/aggregated modular antennaarrays 200), which may be exposed via services provided by the networkservice management platform 163 a (e.g., a Service Management andOrchestration (SMO) platform). Yet further continuing the example, invarious embodiments, the MBE rApp may utilize O1/FH-M plane data tooptimize beamforming/enhancements by configuring RU or DU parameters.Still continuing the example, an MBE xApp (which may be deployed fromthe network service management platform 163 a to the RIC portion 164a-2) may perform beamforming-related actions and/or enhancements basedon data collected from E2 reports.

FIG. 8A depicts a table 800 identifying monitorable parameters forfacilitating MIMO networking in accordance with various aspectsdescribed herein. As shown in FIG. 8A, parameters that may be monitored(“dials”) may relate to scheduler capacity optimization, coverage and/orcapacity optimization (including, for example, capacity optimization viainterference mitigation), and user DL quality optimization.

In various embodiments, data regarding “Scheduled UEs” may be monitored(e.g., in real-time or near real-time). This may include, for example,Quality-of-Service (QoS) Class Identifiers (QCIs) with associatedpriorities, angle of arrival (2D beam #), timing advance (TA), dataradio bearer (DRB) throughput (T-put), SINR, and/or the like. Interfacesbetween the RU/DU and the RIC/CU, etc. may allow a resident DU schedulerto accept inputs and take affirmative actions to incorporate theparallelism afforded by Mu-MIMO scheduling opportunities identified bythe RIC from all sources.

In some embodiments, data regarding “UE Spatial Separability” may bemonitored. In various embodiments, a UE Channel Cross Correlation Matrix(e.g., pairwise for all simultaneously scheduled UEs), such asE{h_(i)*h_(j)}, may be obtained and analyzed to facilitate one or morefunctions/optimizations. For instance, for two UEs, their spatialseparability may relate to an inner product of the UEs' respectiveestimated channels, where there may be orthogonality or nearorthogonality between the two UEs if that inner product is zero or nearzero. In certain embodiments, the inner product may be normalized as acorrelation coefficient that can be used as a measure of orthogonality(where, e.g., a correlation coefficient of ‘1’ may indicate that thereis no orthogonality, and a correlation coefficient that is smaller, suchas near zero, may indicate that there is orthogonality or nearorthogonality). In various embodiments, a correlation threshold relatingto the UE Spatial Separability parameter (e.g., a threshold for thecorrelation coefficient) may be defined and used for scheduler-relatedand/or power control-related determinations.

As an example, the UE Spatial Separability parameter may be useful fordetermining whether a scheduler is under-scheduling UEs, in which caseone or more actions can be taken (e.g., by the system 162 a, such as thevDUs 166 a and/or the vCUs 174 a) to adjust the scheduling, particularlyto include UEs to be scheduled (e.g., with data in their buffers,priority to transmit, etc.) and that are determined to be sufficientlyseparated (e.g., based on the correlation coefficient satisfying (e.g.,being less than or equal to) the correlation threshold).

As another example, there may be instances where a UE, such as UE 501 ofFIG. 5A, at or proximate to a cell edge is experiencing poor/decreasingthroughput (e.g., with a full or near full buffer or with (e.g.,historical) throughput falling to or below a particular threshold), andthus warrants higher scheduler priority for the UE 501. However,considerations may need to be made as to whether the UE 501 should begiven higher scheduling priority over one or more other UEs. Forinstance, a different UE, such as the UE 503 of FIG. 5A, may have highSINR and/or may have a need/demand for high throughput (e.g., the UE 503may correspond to a fixed wireless user or the like with a throughputdemand that is greater than or equal to a throughput threshold and/orwhere SINR of signal(s) associated with the UE 503 is greater than orequal to a signal quality threshold). Where the system 162 a determinesthat the UE 503, from the perspective of the antenna system (e.g., anaggregation of modular antenna arrays 200), is not sufficientlyspatially separated from the UE 501 (e.g., the correlation coefficientis greater than the correlation threshold and thus the UE 502 and the UE503 are not orthogonal or not near orthogonal to one another),prioritizing the UE 501 for scheduling purposes may result in powerfultransmissions being sent for the UE 501 that undesirably interfere withthe UE 503. Here, the system 162 a may utilize the UE SpatialSeparability parameter to determine whether the UE 501 should be givenhigher scheduling priority. For example, in a case where the system 162a determines that the coefficient correlation associated with the UE 501and the UE 503 satisfies the correlation threshold (e.g., is less thanor equal to the correlation threshold), and thus there is likely highseparability between the two UEs, the system 162 a may permit thescheduler to schedule the UE 501 (e.g., may permit the scheduler toschedule the UE 501 for parallel transmissions with the UE 503). In acase where the system 162 a determines that the coefficient correlationassociated with the two UEs does not satisfy the correlation threshold(e.g., where the correlation coefficient is greater than the correlationthreshold), and thus there is likely low separability between the twoUEs, the system 162 a may prevent the scheduler from scheduling the UE501 (e.g., may prevent the scheduler from scheduling the UE 501 forparallel transmissions with the UE 503) or otherwise de-prioritizescheduling of the UE 501.

Continuing the example, in some embodiments, in the case where thesystem 162 a determines that the correlation coefficient does notsatisfy the correlation threshold, the system 162 a may additionally, oralternatively, perform one or more other actions to address the lowseparability, such as adjusting or modulating (e.g., decreasing) the DLtransmit power (e.g., the Downlink Transmit Power Allocation parameterdescribed below) for transmissions directed to/for the UE 501 and/oradjusting or modulating (e.g., increasing) the DL transmit power fortransmissions directed to/for the UE 503, so as to minimize the effectsof interference to the UE 503. In certain embodiments, the system 162 amay be configured to perform additional adjustments to the DL transmitpower for the UE 501 and/or perform adjustments to the DL transmit powerfor the UE 503, based on measurement data (e.g., provided by the UE503). For instance, in a case where measurement data indicates adecrease in signal quality (e.g., a lower SINR or the like), the system162 a may (e.g., further) decrease the DL transmit power for the UE 501and/or may (e.g., further) increase the DL transmit power for the UE503.

In certain embodiments, data regarding a “Coherence Bandwidth” and/or a“Coherence Time” may be monitored (e.g., in real-time or nearreal-time). In one or more embodiments, data regarding “Pilot signallength” and/or “Coherence block” size (e.g., in symbols) mayadditionally, or alternatively, be monitored. In various embodiments,data regarding “Frequency Reuse factor for Pilots” (a pilot reuse factorover N_(reuse) sites or cells) may be monitored. As described above inconnection with various embodiments, such as those relating to FIG. 4A,a coherence block (or coherence bandwidth and coherence time) can beused to determine the number of orthogonal SRS/pilot sequences that maybe used by a cell and/or various neighboring cells. In one or moreembodiments, N_(reuse) can be set based on an estimate of the quality ofa channel (e.g., based on indicators such as a coherency block or thelike). Additionally, pilot reuse factors can identify whether pilots maybe reused, which can increase orthogonality, avoid pilot contaminationamongst a cluster of cells, and/or avoid inter-base station DL and ULinterference in a cluster of cells in which Mu-MIMO, for example, may beemployed.

In some embodiments, data regarding “Indication of MIMO type” may bemonitored (e.g., in real-time or near real-time), which may, forexample, inform whether UL data relates to Mu-MIMO or Su-MIMO.

In certain embodiments, data regarding “Downlink CQI” may be monitored(e.g., in real-time or near real-time), which may indicate amodulation/coding scheme. In various embodiments, data regarding a PMI(which may, for example, include a Rank Indicator—e.g., a number oflayers to be used for DL transmissions to a UE) may be monitored. ThePMI may indicate the precoding matrix to be used for DL transmissions,which may be based on the Rank Indicator.

In one or more embodiments, data regarding “Uplink SINR and EVM” may bemonitored (e.g., in real-time or near real-time).

In some embodiments, data regarding “Uplink Covariance” may be monitored(e.g., in real-time or near real-time). This may include an ULCovariance matrix of a received signal after removal of pilot(s).

In certain embodiments, data regarding “Condition Number” may bemonitored (e.g., in real-time or near real-time). A condition number(e.g., in dB), or a channel condition number, may relate to estimationof performance of a MIMO channel.

FIG. 8B depicts a table 810 identifying controllable parameters forfacilitating MIMO networking in accordance with various aspectsdescribed herein. As shown in FIG. 8B, parameters that may be controlledmay relate to user quality optimization (including, for example, UL/DLuser quality optimization), scheduler optimization, and capacityoptimization.

In various embodiments, an “Uplink UE Transmit Power Control” parametermay be controllable (e.g., in real-time or near real-time). In exemplaryembodiments, controlling UE transmit power may be based upon obtaining(e.g., in real-time or near real-time) an internal UL UE SINRmeasurement, where a policy may be to increase the UE transmit power upto a (e.g., mobile network operator) specified minimum SINR level. Insome cases, such as where there is an ongoing high data ratetransmission (e.g., at 100 megabits per second (Mbps)), the UE transmitpower may or may not be increased (e.g., may not be increased in orderto conserve overall power resources for the UE). In various embodiments,a determination as to whether to control (e.g., increase or decrease) UEtransmit power for a particular UE may depend on operation mode (such aswhether Mu-MIMO is being employed, etc.), orthogonality between theparticular UE and other UEs, and/or the like.

In exemplary embodiments, the system 162 a (e.g., the vDUs 166 a and/orthe vCUs 174 a) may control UL UE transmit power for UEs in Mu-MIMO modein cases where one of the UEs' UL transmissions are determined to likelyinterfere with those of another one of the UEs. For example, assume thatthe system 162 a schedules parallel transmissions for the UEs 501, 502,and 503 in Mu-MIMO in a particular time slot and over a certain numberof PRBs. As part of facilitating such transmissions, the system 162 amay generate UL combining weights that cause antenna elements to “point”toward the UE 501 for receiving UE 501's transmissions and tosimultaneously null (e.g., spatially filter) UL transmissions from theUEs 502 and 503, may generate UL combining weights that cause antennaelements to “point” toward the UE 502 for receiving UE 502'stransmissions and to simultaneously null UL transmissions from the UEs501 and 503, and so on. Where one of the UEs, such as the UE 502 (e.g.,a LOS UE with minimal path loss), is much closer to the modular antennaarrays 200 than another one of the UEs, such as the UE 501, the ULsignal strength of the UE 502 may be much larger than that of the UE 501(e.g., a difference between the signal strengths may be larger than orequal to a threshold). For instance, the UL signal strength of the UE502 may be 50 dB greater than that of the UE 501. In such a case, it maybe difficult for the system 162 a or the modular antenna arrays 200 tonull the UE 502's transmissions when receiving UE 501's transmissions.For example, the system 162 a or the modular antenna arrays 200 may onlybe able to generate a 30 dB null on the UE 502's transmissions, whichmay still leave the UE 501's transmissions at −20 dB relative to the UE502's transmissions. Therefore, in certain embodiments, the system 162 amay instruct one UE to adjust (e.g., increase) its UL transmit powerand/or instruct another UE to adjust (e.g., decrease) its UL transmitpower to compensate for disparities between the two UEs' UL transmitsignal strengths. In the foregoing example, the system 162 a mayinstruct the UE 501 to increase its UL transmit power and/or instructthe UE 502 to decrease its UL transmit power to a point where the system162 a and/or the modular antenna arrays 200 are able to properly receiveUE 501's transmissions (e.g., where the UE 501's transmissions are at ornear 0 dB relative to the UE 502's transmissions).

In some cases, depending on where a UE is located, increasing that UE'sUL transmit power may negatively impact a neighboring cell's operations.In various embodiments, orthogonal SRS/pilot sequences may be employed,as described elsewhere herein, to alleviate such an impact.

In some embodiments, a “Downlink Transmit Power Allocation” parameter(e.g., as described above) may be controllable (e.g., in real-time ornear real-time). In exemplary embodiments, controlling transmit power toa UE may be based upon obtaining a downlink UE internal SINRmeasurement, where a policy may be to increase the transmit power to theUE in a case where the downlink UE internal SINR falls below a (e.g.,mobile network operator) specified minimum SINR level. In some cases,the system 162 a (e.g., the vDUs 166 a and/or the vCUs 174 a) maydetermine and form null patterns as part of mitigating interference to afirst UE caused by transmissions for a second UE. Where a null patternfor the first UE satisfies (e.g., reaches) a deepest/highest possiblelevel (dependent on the number of antenna weights, user separation,etc.), the transmit power to the second UE may be reduced as a furthermitigative action.

In Mu-MIMO, for example, the power that is allocated for transmittingparallel streams can impact the resulting quality of the parallelstreams when they are received (e.g., by a base station or by UEs).Where a base station, such as the system 162 a (e.g., the vDUs 166 aand/or the vCUs 174 a), has limited information regarding Mu-MIMO streamcarriers, proper power allocation for individual streams can be lessthan optimal. In exemplary embodiments, the system 162 a (e.g., the vDUs166 a and/or the vCUs 174 a) may adjust Mu-MIMO downlink and/or uplinkpower allocations based on measurements relating to UE signal quality(e.g., KPIs provided by UEs or the like). In some embodiments, thesystem 162 a can define power allocation policies for base stationequipment to follow based on the measurements (which may, for example,involve rapid dynamic adjustments/reconfigurations needed for efficientMu-MIMO). In various embodiments, parameters that can be monitored toinform Mu-MIMO downlink and/or uplink power allocations may include, forexample, downlink CQI, uplink SINR, error vector magnitude (EVM),scheduled UEs, UE spatial separability, etc. In some embodiments,various parameters may be modified as part of adjusting Mu-MIMO downlinkand/or uplink power allocations, such as, for example, pilot sequences,pilot sequence distribution, MIMO modes, Su-MIMO rank, etc.

In certain embodiments, a “Parallel Scheduling Control” parameter may becontrollable. This may relate to capacity (e.g., the one minute averagenumber K of parallel scheduled UEs) and may be specified by one or morerequired UE separability thresholds—i.e., an UL threshold and/or a DLthreshold—that indicate how far UEs are to be separated from one anotherin geolocation. In various embodiments, the Parallel Scheduling Controlparameter may be adjusted such that a correlation threshold—e.g.,described above with respect to the UE Spatial Separability parameter ofFIG. 8A—may be modified in order to increase the number of UEs that areeligible for parallel transmissions. As an example, assume that thecorrelation threshold is 0.3. Continuing the example, assume that, forthe UEs 501 to 505 of FIG. 5A, the correlation coefficient between UE501 and UE 503 is 0.7, the correlation coefficient between UE 504 and UE505 is 0.1, etc., where the largest correlation coefficient betweenvarious pairs of the UEs 501 to 505 is 0.7 (e.g., the correlationcoefficient between UE 501 and UE 503). Here, the system 162 a (e.g.,the vDUs 166 a and/or the vCUs 174 a) may adjust the correlationthreshold, such as by increasing the correlation threshold from 0.3 to0.7, 0.71, or the like, so as to enable some or all of the UEs 501 to505 to be eligible for parallel transmissions or to be simultaneouslyserved. In this way, PRBs may be reused for multiple UEs (e.g., all fiveof UEs 501 to 505 shown in FIG. 5A), and capacity can be significantlyincreased in the cell (here, e.g., five times the capacity in a casewhere transmissions are otherwise made for only a single one of theUEs). Controlling the Parallel Scheduling Control parameter and/or thecorrelation threshold relating to UE Spatial Separability can avoid anyartificial control (e.g., by a third-party vendor or the like) of thequantity of UEs that may be eligible, or considered, for paralleltransmissions.

In Mu-MIMO, overlapping streams can interfere with one another, and thelikelihood of such interference may vary depending on the circumstances.In exemplary embodiments, the system 162 a (e.g., the vDUs 166 a and/orthe vCUs 174 a) may adjust the number of parallel streams (e.g., thatoverlap in frequency and time) or existing connections for a group ofUEs in Mu-MIMO mode based on measurements relating to channel quality ofindividual UEs in the group of UEs. For example, in a case where thesystem 162 a determines that signal quality is low for a UE (e.g., poorSRS or the like, shrinking coherence block, etc.), the system 162 a mayreduce the number of parallel streams (e.g., capacity) for that UEand/or for one or more other UEs that are also in Mu-MIMO mode. Asanother example, in a case where the system 162 a determines that signalquality is high for a UE (e.g., adequate SRS or the like, largecoherence block, etc.), the system 162 a may increase the number ofparallel streams for that UE and/or for one or more other UEs that arealso in Mu-MIMO mode. In one or more embodiments, the system 162 a mayperform such adjustments dynamically (e.g., based on results fromanalyzing obtained data) and/or periodically, in accordance with one ormore feedback loops. In various embodiments, parameters that can bemonitored to inform adjustments to the number of parallel streams mayinclude, for example, scheduled UEs, UE spatial separability, downlinkCQI, etc. In some embodiments, parallel scheduling control parameter(s)may be modified (e.g., raised or lowered) as part of adjusting thenumber of parallel streams.

In one or more embodiments, a “Pilot Sequence” parameter may becontrollable. In certain embodiments, pilot sequence lengths and/ornumbers of pilot sequences may be set to ensure that overhead isminimized while also avoiding pilot contamination.

In various embodiments, a “Pilot Sequence distribution” parameter may becontrollable (e.g., in real-time or near real-time). As described abovewith respect to FIGS. 4A and 4B, for example, available orthogonalSRS/pilot sequences may be partitioned and distributed to N_(reuse)surrounding cells.

In some embodiments, an “Ability to set MIMO modes” parameter may becontrollable (e.g., in real-time or near real-time). MIMO modes mayinclude, for example, Su-MIMO only, Mu-MIMO only, both, by QCI, or thelike. As described above with respect to at least FIG. 4A, Mu-MIMO maybe more suitable for LOS or near LOS/stationary or near stationary(e.g., fixed wireless) UEs (which may have buffers that are continuouslyfull or near full and/or may have coherence blocks that are relativelylarge (e.g., larger than a threshold number of symbols)).Implementations of MIMO networking described herein provide Mu-MIMO tosuch UEs in a transparent manner to other UEs (e.g., in Su-MIMO mode),such as UEs in motion or that have NLOS. The ability to set MIMO modesenables the network to differentiate UEs that may be eligible forMu-MIMO from UEs that may not.

In exemplary embodiments, eligibility for Mu-MIMO may be based onmobility or predicted mobility of a UE. For example, lower mobilityusers (e.g., wireless users with UEs that are stationary or nearstationary or that are not predicted to move significantly (e.g.,predicted to move at less than a threshold speed or the like)) maygenerally have high network resource usage requirements, such as forvideo applications, etc., where buffers for such UEs may be constantlyfull or near full, and thus may require simultaneous scheduling to avoidcongestion. Low-mobility users may thus be better served via Mu-MIMO ascompared to other types of techniques, such as Su-MIMO. In contrast,high-mobility users typically use applications with periodic, shortbursts of packets, where buffers are not as full as with fixed usage.With minimal to no periodic channel change (e.g., large coherenceblocks), UL channel estimations are generally more accurate forlow-mobility users, and thus yield more capacity.

In some cases, treating all UEs equally (as may often be done)—that is,employing the same network mode for all UEs regardless of their mobilityor predicted mobility—can actually degrade network performance. Forexample, whereas fixed wireless users may not require frequent channelestimation as UEs with higher mobility (e.g., as can be seen fromvarious embodiments described herein), performing typical channelestimation for fixed wireless users may increase network load and thusnegatively impact network performance for higher mobility UEs.Therefore, beyond increasing performance for fixed wireless users, theability to set MIMO modes for different UEs, including where SRS-relatedoverhead is reduced, can improve network capacity in a given cell.

In various embodiments, parameters that can be monitored to informdeterminations regarding MIMO modes may include, for example, downlinkCQI, condition number, coherence time, pilot frequency reuse factor,pilot and coherence block, etc.

In certain embodiments, a “Set Su-MIMO Rank” parameter may becontrollable. In various embodiments, the system 162 a (e.g., the vDUs166 a and/or the vCUs 174 a) may calculate (e.g., internally inreal-time or near real-time) a condition number (or rank). A thresholdfor the condition number may be defined (e.g., by a mobile networkoperator), which may be used to derive a base station-assigned Su-MIMOrank from a UE-reported Su-MIMO rank that the UE may have requestedbased on DL measurements. A higher Su-MIMO rank may correspond to alarger number of layers or parallel streams for a UE. In variousembodiments, the system 162 a may identify a precoding vector, which theUE may utilize to distinguish between layers.

In some embodiments, parameters that can be monitored to informdeterminations regarding Su-MIMO ranks may include, for example,downlink CQI, channel condition number, etc. In various embodiments, thesystem 162 a may additionally be capable of setting a rank for UEs inMu-MIMO mode. Ranks for a UE in Mu-MIMO mode may include rank 4, rank 8,etc.

In one or more embodiments, an “Insert Quiescent Antenna Weights”parameter may be controllable (e.g., in real-time or near real-time).This may enable arbitrary quiescent (e.g., static) weights for antennaelements (e.g., antenna elements 202) that are to be convolved or moreefficiently multiplied in the beam space with calculated weights foreither the UL or the DL.

FIG. 8C depicts an illustrative embodiment of a method 820 in accordancewith various aspects described herein. In some embodiments, one or moreprocess blocks of FIG. 8C can be performed by a RAN or system, such asthe system 162 a. In some embodiments, one or more process blocks ofFIG. 8C may be performed by another device or a group of devicesseparate from or including the system 162 a, such as the network servicemanagement platform 163 a, the RIC 164 a, the CU 174 a, one or more DUs166 a, one or more RUs 168 a, and/or the core network 190.

At 820 a, the method can include causing a set of antenna elements of afirst modular antenna array to transmit a set of signals, wherein thefirst modular antenna array comprises a first plurality of antennaelements that includes the set of antenna elements. For example, thesystem 162 a can, in a manner similar to that described elsewhereherein, perform one or more operations that include causing a set ofantenna elements of a first modular antenna array to transmit a set ofsignals, wherein the first modular antenna array comprises a firstplurality of antenna elements that includes the set of antenna elements.

At 820 b, the method can include, responsive to a second antenna elementof a second modular antenna array receiving the set of signals,determining a location of the second antenna element, wherein the secondmodular antenna array comprises a second plurality of antenna elementsthat includes the second antenna element. For example, the system 162 acan, in a manner similar to that described elsewhere herein, perform oneor more operations that include, responsive to a second antenna elementof a second modular antenna array receiving the set of signals,determining a location of the second antenna element, wherein the secondmodular antenna array comprises a second plurality of antenna elementsthat includes the second antenna element.

At 820 c, the method can include determining, in accordance with thelocation of the second antenna element and in accordance with knowndistances between the second antenna element and each other antennaelement of the second plurality of antenna elements, inter-elementpropagation delays between the first plurality of antenna elements andthe second plurality of antenna elements. For example, the system 162 acan, in a manner similar to that described elsewhere herein, perform oneor more operations that include determining, in accordance with thelocation of the second antenna element and in accordance with knowndistances between the second antenna element and each other antennaelement of the second plurality of antenna elements, inter-elementpropagation delays between the first plurality of antenna elements andthe second plurality of antenna elements.

At 820 d, the method can include causing a reference antenna element ofthe first plurality of antenna elements to transmit a first referencesignal. For example, the system 162 a can, in a manner similar to thatdescribed elsewhere herein, perform one or more operations that includecausing a reference antenna element of the first plurality of antennaelements to transmit a first reference signal.

At 820 e, the method can include, based on the first reference signal,determining, for each antenna element of the second plurality of antennaelements, a respective receive-related phase offset relative to thereference antenna element based on one or more of the inter-elementpropagation delays, resulting in a plurality of respectivereceive-related phase offsets. For example, the system 162 a can, in amanner similar to that described elsewhere herein, perform one or moreoperations that include, based on the first reference signal,determining, for each antenna element of the second plurality of antennaelements, a respective receive-related phase offset relative to thereference antenna element based on one or more of the inter-elementpropagation delays, resulting in a plurality of respectivereceive-related phase offsets.

At 820 f, the method can include causing each antenna element of thesecond plurality of antenna elements to transmit a respective secondreference signal, resulting in a plurality of second reference signals.For example, the system 162 a can, in a manner similar to that describedelsewhere herein, perform one or more operations that include causingeach antenna element of the second plurality of antenna elements totransmit a respective second reference signal, resulting in a pluralityof second reference signals.

At 820 g, the method can include, responsive to the reference antennaelement receiving the plurality of second reference signals, determininga respective transmit-related phase offset for each antenna element ofthe second plurality of antenna elements relative to the referenceantenna element based on one or more of the inter-element propagationdelays, resulting in a plurality of respective transmit-related phaseoffsets. For example, the system 162 a can, in a manner similar to thatdescribed elsewhere herein, perform one or more operations that include,responsive to the reference antenna element receiving the plurality ofsecond reference signals, determining a respective transmit-relatedphase offset for each antenna element of the second plurality of antennaelements relative to the reference antenna element based on one or moreof the inter-element propagation delays, resulting in a plurality ofrespective transmit-related phase offsets.

At 820 h, the method can include performing a calibration between thefirst modular antenna array and the second modular antenna array basedon the plurality of respective receive-related phase offsets and theplurality of respective transmit-related phase offsets. For example, thesystem 162 a can, in a manner similar to that described elsewhereherein, perform one or more operations that include performing acalibration between the first modular antenna array and the secondmodular antenna array based on the respective receive-related phaseoffsets and the respective transmit-related phase offsets.

In some implementations of these embodiments, the first plurality ofantenna elements is calibrated with one another, the second plurality ofantenna elements is calibrated with one another, and the performing thecalibration results in the first modular antenna array and the secondmodular antenna array forming a coherent antenna system.

In some implementations of these embodiments, the set of antennaelements comprises at least three antenna elements, the set of signalscomprises at least three signals, and the determining the location ofthe second antenna element is based on multilateration.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include determining an offset of afirst plane of the first modular antenna array relative to a secondplane of the second modular antenna array based on the location of thesecond antenna element, a location of a third antenna element of thesecond modular antenna array, and a location of a fourth antenna elementof the second modular antenna array.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include identifying the referenceantenna element.

In some implementations of these embodiments, each antenna element ofthe first plurality of antenna elements and the second plurality ofantenna elements is associated with a respective transmitter.

In some implementations of these embodiments, each antenna element ofthe first plurality of antenna elements and the second plurality ofantenna elements is associated with a respective receiver.

In some implementations of these embodiments, the first modular antennaarray and the second modular antenna array are operable in multi-user(Mu)-multiple-input-multiple-output (MIMO) mode, single-user (Su)-MIMOmode, or a combination thereof.

In some implementations of these embodiments, the first modular antennaarray and the second modular antenna array are operable in time divisionduplex (TDD), frequency division duplex (FDD), or a combination thereof.

In some implementations of these embodiments, the first plurality ofantenna elements and the second plurality of antenna elements employradio frequency (RF) complementary metal-oxide-semiconductor (CMOS)technology.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 8C, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In various embodiments, a device may include a processing systemincluding a processor, and a memory that stores executable instructionsthat, when executed by the processing system, facilitate performance ofoperations. The operations may include causing a reference antennaelement of a first plurality of antenna elements of a first modularantenna panel to transmit a first signal. The operations may furtherinclude deriving, for each antenna element of a second plurality ofantenna elements of a second modular antenna panel, a respectivereceive-related phase offset, relative to the reference antenna element,based on inter-element propagation delays between the first plurality ofantenna elements and the second plurality of antenna elements, resultingin a plurality of respective receive-related phase offsets. Theoperations may further include causing each antenna element of thesecond plurality of antenna elements to transmit a respective secondsignal, resulting in a plurality of second signals. The operations mayfurther include, responsive to the reference antenna element receivingthe plurality of second signals, determining a respectivetransmit-related phase offset for each antenna element of the secondplurality of antenna elements, relative to the reference antennaelement, based on the inter-element propagation delays between the firstplurality of antenna elements and the second plurality of antennaelements, resulting in a plurality of respective transmit-related phaseoffsets. The operations may further include calibrating the firstmodular antenna panel with the second modular antenna panel based on theplurality of respective receive-related phase offsets and the pluralityof respective transmit-related phase offsets.

In some implementations of these embodiments, the operations may furtherinclude emitting, via a group of antenna elements of the first pluralityof antenna elements, a first group of signals, and estimating respectivelocations of a second antenna element of the second plurality of antennaelements, a third antenna element of the second plurality of antennaelements, and a fourth antenna element of the second plurality ofantenna elements.

In some implementations of these embodiments, the operations may furtherinclude, based on the estimating, determining, in accordance with knowninter-element distances of the second plurality of antenna elements, theinter-element propagation delays between the first plurality of antennaelements and the second plurality of antenna elements.

In some implementations of these embodiments, the operations may furtherinclude determining an offset between a first plane of the first modularantenna panel and a second plane of the second modular antenna panelbased on the respective locations of the second antenna element, thethird antenna element, and the fourth antenna element.

In some implementations of these embodiments, use of the offset betweenthe first plane and the second plane enables the first modular antennapanel and the second modular antenna panel to operate as a coherentantenna system.

In some implementations of these embodiments, the causing the referenceantenna element to transmit the first signal, the deriving, the causingeach antenna element of the second plurality of antenna elements totransmit the respective second signal, the determining, and thecalibrating the first modular antenna panel with the second modularantenna panel are performed periodically.

In various embodiments, a method may include causing, by a processingsystem including a processor, a reference antenna element of a first setof antenna elements of a first modular antenna array to transmit a firstreference signal. The method may further include determining, by theprocessing system, for each antenna element of a second set of antennaelements of a second modular antenna array, a respective receive-relatedoffset, relative to the reference antenna element, based oninter-element propagation delays between the first set of antennaelements and the second set of antenna elements, resulting in aplurality of respective receive-related offsets. The method may furtherinclude causing, by the processing system, each antenna element of thesecond set of antenna elements to transmit a respective second referencesignal, resulting in a plurality of second reference signals. The methodmay further include, based on the reference antenna element receivingthe plurality of second reference signals, calculating, by theprocessing system, a respective transmit-related offset for each antennaelement of the second set of antenna elements relative to the referenceantenna element based on the inter-element propagation delays betweenthe first set of antenna elements and the second set of antennaelements, resulting in a plurality of respective transmit-relatedoffsets. The method may further include performing, by the processingsystem, a calibration between the first modular antenna array and thesecond modular antenna array based on the plurality of respectivereceive-related offsets and the plurality of respective transmit-relatedoffsets.

In some implementations of these embodiments, the method may furtherinclude identifying the reference antenna element.

In some implementations of these embodiments, each antenna element ofthe first set of antenna elements and the second set of antenna elementsis associated with a respective transmitter.

In some implementations of these embodiments, each antenna element ofthe first set of antenna elements and the second set of antenna elementsis associated with a respective receiver.

In some implementations of these embodiments, the first set of antennaelements and the second set of antenna elements employ radio frequency(RF) complementary metal-oxide-semiconductor (CMOS) technology.

FIG. 8D depicts an illustrative embodiment of a method 822 in accordancewith various aspects described herein. In some embodiments, one or moreprocess blocks of FIG. 8D can be performed by a RAN or system, such asthe system 162 a. In some embodiments, one or more process blocks ofFIG. 8D may be performed by another device or a group of devicesseparate from or including the system 162 a, such as the network servicemanagement platform 163 a, the RIC 164 a, the CU 174 a, one or more DUs166 a, one or more RUs 168 a, and/or the core network 190.

At 822 a, the method can include receiving sounding reference signal(SRS) symbols from antenna elements of each of multiple modular antennaarrays, wherein the multiple modular antenna arrays are operativelycombined to form a coherent antenna system. For example, the system 162a can, in a manner similar to that described elsewhere herein, performone or more operations that include receiving sounding reference signal(SRS) symbols from antenna elements of each of multiple modular antennaarrays, wherein the multiple modular antenna arrays are operativelycombined to form a coherent antenna system.

At 822 b, the method can include performing an uplink (UL) channelestimation and a downlink (DL) channel estimation, across a plurality ofphysical resource blocks (PRBs), based on the SRS symbols. For example,the system 162 a can, in a manner similar to that described elsewhereherein, perform one or more operations that include performing an uplink(UL) channel estimation and a downlink (DL) channel estimation, across aplurality of physical resource blocks (PRBs), based on the SRS symbols.

At 822 c, the method can include calculating, for the antenna elements,a plurality of uplink (UL) combining weights based on the UL channelestimation and a plurality of downlink (DL) precoder weights based onthe DL channel estimation. For example, the system 162 a can, in amanner similar to that described elsewhere herein, perform one or moreoperations that include calculating, for the antenna elements, aplurality of uplink (UL) combining weights based on the UL channelestimation and a plurality of downlink (DL) precoder weights based onthe DL channel estimation.

At 822 d, the method can include causing the plurality of UL combiningweights and the plurality of DL precoder weights to be applied to theantenna elements, thereby adjusting beamforming of the coherent antennasystem. For example, the system 162 a can, in a manner similar to thatdescribed elsewhere herein, perform one or more operations that includecausing the plurality of UL combining weights and the plurality of DLprecoder weights to be applied to the antenna elements, therebyadjusting beamforming of the coherent antenna system.

In some implementations of these embodiments, the calculating theplurality of UL combining weights and the plurality of DL precoderweights comprises performing matrix inversions across the coherentantenna system as a function of PRB, user equipment (UE), layer data, ora combination thereof.

In some implementations of these embodiments, the performing the ULchannel estimation and the DL channel estimation are based on one ormore coherence block indicators.

In some implementations of these embodiments, the one or more coherenceblock indicators are associated with one or more user equipment (UE).

In some implementations of these embodiments, the plurality of PRBs isin the frequency domain.

In some implementations of these embodiments, the performing the ULchannel estimation comprises performing interpolation for one or morePRBs of the plurality of PRBs.

In some implementations of these embodiments, the multiple modularantenna arrays are operated in multi-user(Mu)-multiple-input-multiple-output (MIMO) mode.

In some implementations of these embodiments, the multiple modularantenna arrays are operated in single-user (Su)-MIMO mode.

In some implementations of these embodiments, the multiple modularantenna arrays are operated in time division duplex (TDD), frequencydivision duplex (FDD), or a combination thereof.

In some implementations of these embodiments, the antenna elementscomprise all of the antenna elements of the multiple modular antennaarrays.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 8D, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In various embodiments, a method may include obtaining, by a cluster ofvirtual distributed units (vDUs) including a plurality of processors,pilot signals from each antenna element of each modular antenna array ofa combination of coherent modular antenna arrays. The method may furtherinclude estimating, by the cluster of vDUs and per physical resourceblock (PRB) of a plurality of PRBs, an uplink (UL) channel based oncross-correlation of the pilot signals with reference tones, andestimating a downlink (DL) channel. The method may further includedetermining, by the cluster of vDUs and based on the estimating the ULchannel, a respective uplink (UL) weight for each antenna element ofeach modular antenna array of the combination of coherent modularantenna arrays, resulting in a set of UL weights. The method may furtherinclude calculating, by the cluster of vDUs and based on the estimatingthe DL channel, a respective downlink (DL) weight for each antennaelement of each modular antenna array of the combination of coherentmodular antenna arrays, resulting in a set of DL weights. The method mayfurther include applying, by the cluster of vDUs, the set of UL weightsand the set of DL weights to the combination of coherent modular antennaarrays, thereby adjusting beamforming of the combination of coherentmodular antenna arrays.

In some implementations of these embodiments, the cluster of vDUs islocated at a pooling site remote from the combination of coherentmodular antenna arrays.

In some implementations of these embodiments, the pilot signals comprisesounding reference signals (SRS).

In some implementations of these embodiments, the obtaining, theestimating the UL channel, the estimating the DL channel, thedetermining, the calculating, and the applying are performedperiodically.

In some implementations of these embodiments, the determining involvesmatrix inversions, and wherein individual vDUs of the cluster of vDUsshare or participate in determining of the matrix inversions.

In various embodiments, a non-transitory machine-readable medium maycomprise executable instructions that, when executed by a processingsystem operatively coupled to a combination of modular antenna panelsand including a processor, facilitate performance of operations. Theoperations may include obtaining sounding reference signal (SRS) symbolsfrom antenna elements of the combination of modular antenna panels. Theoperations may further include performing an uplink (UL) channelestimation, across a plurality of physical resource blocks (PRBs), usingthe SRS symbols. The operations may further include predicting adownlink (DL) channel, across the plurality of PRBs, using the SRSsymbols, resulting in a predicted DL channel. The operations may furtherinclude deriving, for the antenna elements, UL combining weights basedon the UL channel estimation and DL precoder weights based on thepredicted DL channel. The operations may further include causing the ULcombining weights and the DL precoder weights to be applied to theantenna elements of the combination of modular antenna panels.

In some implementations of these embodiments, the combination of modularantenna panels is operated in multi-user(Mu)-multiple-input-multiple-output (MIMO) mode, single-user (Su)-MIMOmode, or a combination thereof.

In some implementations of these embodiments, the combination of modularantenna panels is operated in time division duplex (TDD), frequencydivision duplex (FDD), or a combination thereof.

In some implementations of these embodiments, the antenna elementscomprise all of the antenna elements of the combination of modularantenna panels.

In some implementations of these embodiments, the performing the ULchannel estimation and the predicting the DL channel are based on one ormore coherence block indicators.

FIG. 8E depicts an illustrative embodiment of a method 824 in accordancewith various aspects described herein. In some embodiments, one or moreprocess blocks of FIG. 8E can be performed by a RAN or system, such asthe system 162 a. In some embodiments, one or more process blocks ofFIG. 8E may be performed by another device or a group of devicesseparate from or including the system 162 a, such as the network servicemanagement platform 163 a, the RIC 164 a, the CU 174 a, one or more DUs166 a, one or more RUs 168 a, and/or the core network 190.

At 824 a, the method can include identifying a coherence time for a userequipment (UE). For example, the system 162 a can, in a manner similarto that described elsewhere herein, perform one or more operations thatinclude identifying a coherence time for a user equipment (UE). Invarious embodiments, the system 162 a may include multiple adaptiveantenna arrays that operate as a coherent antenna system.

At 824 b, the method can include identifying a coherence bandwidth forthe UE. For example, the system 162 a can, in a manner similar to thatdescribed elsewhere herein, perform one or more operations that includeidentifying a coherence bandwidth for the UE.

At 824 c, the method can include determining a coherence block based onthe coherence time and the coherence bandwidth. For example, the system162 a can, in a manner similar to that described elsewhere herein,perform one or more operations that include determining a coherenceblock based on the coherence time and the coherence bandwidth.

At 824 d, the method can include, based on a first determination thatthe coherence block satisfies a threshold, permitting the UE to transmitsounding reference signal (SRS) data over a smaller SRS bandwidth thatis smaller than a default SRS bandwidth, at a lower periodicity that islower than a default periodicity, or a combination thereof, therebyconserving power resources of the UE. For example, the system 162 a can,in a manner similar to that described elsewhere herein, perform one ormore operations that include, based on a first determination that thecoherence block satisfies a threshold, permitting the UE to transmitsounding reference signal (SRS) data over a smaller SRS bandwidth thatis smaller than a default SRS bandwidth, at a lower periodicity that islower than a default periodicity, or a combination thereof, therebyconserving power resources of the UE.

In some implementations of these embodiments, the UE is located at orwithin a threshold distance from a cell edge, and the system 162 a mayperform one or more operations that include, based on the firstdetermination that the coherence block satisfies the threshold,exploiting the coherence block by determining an average across thecoherence block to obtain an uplink (UL) channel estimate for the UE,thereby maintaining UL coverage for the UE.

In some implementations of these embodiments, the permitting comprisesinstructing the UE to transmit the SRS data over the smaller SRSbandwidth, at the lower periodicity, or the combination thereof.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include, based on the firstdetermination that the coherence block satisfies the threshold,determining that the UE is eligible for multi-user(Mu)-multiple-input-multiple-output (MIMO).

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include employing Mu-MIMO for the UEbased on the determining that the UE is eligible for Mu-MIMO.

In some implementations of these embodiments, the employing Mu-MIMO forthe UE is performed transparently to other UEs for which single-user(Su)-MIMO is employed.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include, based on a seconddetermination that the coherence block does not satisfy the threshold,determining that the UE is not eligible for multi-user(Mu)-multiple-input-multiple-output (MIMO).

In some implementations of these embodiments, the threshold comprises anumber of SRS symbols.

In some implementations of these embodiments, the multiple adaptiveantenna arrays operate in time division duplex (TDD), frequency divisionduplex (FDD), or a combination thereof.

In some implementations of these embodiments, the identifying thecoherence time and the coherence bandwidth comprises obtaininginformation regarding the coherence time and the coherence bandwidth viaan Open RAN (O-RAN) compliant interface.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 8E, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In various embodiments, a non-transitory machine-readable medium maycomprise executable instructions that, when executed by a processingsystem communicatively coupled with a combination of modular antennapanels and including a processor, facilitate performance of operations.The operations may include tracking a coherence block for a userequipment (UE) based on information obtained from a RAN interface. Theoperations may further include identifying, based on the tracking, thatthe coherence block does not satisfy a threshold. The operations mayfurther include, based on the identifying that the coherence block doesnot satisfy the threshold, instructing the UE to transmit soundingreference signals (SRS) over a larger SRS bandwidth that is larger thana default SRS bandwidth, at a higher periodicity that is higher than adefault periodicity, or a combination thereof, and determining that theUE is not eligible for multi-user (Mu)-multiple-input-multiple-output(MIMO).

In some implementations of these embodiments, each modular antenna panelof the combination of modular antenna panels comprises a respectivegroup of antenna elements, resulting in multiple respective groups ofantenna elements, and the antenna elements of the multiple respectivegroups of antenna elements are coherent with one another.

In some implementations of these embodiments, the combination of modularantenna panels operates in time division duplex (TDD), frequencydivision duplex (FDD), or a combination thereof.

In some implementations of these embodiments, the RAN interface conformsto Open RAN (O-RAN) standards.

In some implementations of these embodiments, the threshold comprises anumber of SRS symbols.

In various embodiments, a method may include identifying, by aprocessing system including a processor, a coherence time and acoherence bandwidth for a user equipment (UE), wherein the UE is locatedat or within a threshold distance from a cell edge. The method mayfurther include determining, by the processing system, a coherence blockbased on the coherence time and the coherence bandwidth. The method mayfurther include, based on a determination that the coherence blocksatisfies a threshold, exploiting, by the processing system, thecoherence block by determining an average across the coherence block toobtain an uplink (UL) channel estimate for the UE, thereby maintainingUL coverage for the UE and enabling multi-user(Mu)-multiple-input-multiple-output (MIMO).

In some implementations of these embodiments, the method may furtherinclude, based on the determination that the coherence block satisfiesthe threshold, permitting the UE to transmit sounding reference signal(SRS) data over a smaller SRS bandwidth, at a lower periodicity, or acombination thereof, thereby conserving power resources of the UE.

In some implementations of these embodiments, the permitting comprisesinstructing the UE to transmit the SRS data over the smaller SRSbandwidth, at the lower periodicity, or the combination thereof.

In some implementations of these embodiments, the processing system iscommunicatively coupled with an aggregation of modular antenna arraysthat operate in time division duplex (TDD), frequency division duplex(FDD), or a combination thereof.

In some implementations of these embodiments, the identifying thecoherence time and the coherence bandwidth comprises obtaining thecoherence time and the coherence bandwidth via an Open Radio AccessNetwork (O-RAN)-compliant interface.

FIG. 8F depicts an illustrative embodiment of a method 826 in accordancewith various aspects described herein. In some embodiments, one or moreprocess blocks of FIG. 8F can be performed by a RAN or system, such asthe system 162 a. In some embodiments, one or more process blocks ofFIG. 8F may be performed by another device or a group of devicesseparate from or including the system 162 a, such as the network servicemanagement platform 163 a, the RIC 164 a, the CU 174 a, one or more DUs166 a, one or more RUs 168 a, and/or the core network 190.

At 826 a, the method can include determining a coherence block for eachuser equipment (UE) of a plurality of UEs being served by a first cell,resulting in a plurality of coherence blocks. For example, the system162 a can, in a manner similar to that described elsewhere herein,perform one or more operations that include determining a coherenceblock for each user equipment (UE) of a plurality of UEs being served bya first cell, resulting in a plurality of coherence blocks.

At 826 b, the method can include, responsive to the determining,identifying a smallest coherence block from the plurality of coherenceblocks. For example, the system 162 a can, in a manner similar to thatdescribed elsewhere herein, perform one or more operations that include,responsive to the determining, identifying a smallest coherence blockfrom the plurality of coherence blocks.

At 826 c, the method can include identifying a pilot sequence lengthbased on the smallest coherence block. For example, the system 162 acan, in a manner similar to that described elsewhere herein, perform oneor more operations that include identifying a pilot sequence lengthbased on the smallest coherence block.

At 826 d, the method can include determining a plurality of orthogonalpilot sequences based on the identifying the pilot sequence length. Forexample, the system 162 a can, in a manner similar to that describedelsewhere herein, perform one or more operations that includedetermining a plurality of orthogonal pilot sequences based on theidentifying the pilot sequence length.

At 826 e, the method can include designating, from the plurality oforthogonal pilot sequences, a first group of orthogonal pilot sequencesfor use in the first cell. For example, the system 162 a can, in amanner similar to that described elsewhere herein, perform one or moreoperations that include designating, from the plurality of orthogonalpilot sequences, a first group of orthogonal pilot sequences for use inthe first cell.

At 826 f, the method can include distributing, to each neighboring cellof a plurality of neighboring cells adjacent to the first cell, arespective group of orthogonal pilot sequences from a remainder of theplurality of orthogonal pilot sequences, to prevent pilot contaminationbetween the first cell and the plurality of neighboring cells. Forexample, the system 162 a can, in a manner similar to that describedelsewhere herein, perform one or more operations that includedistributing, to each neighboring cell of a plurality of neighboringcells adjacent to the first cell, a respective group of orthogonal pilotsequences from a remainder of the plurality of orthogonal pilotsequences, to prevent pilot contamination between the first cell and theplurality of neighboring cells.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include identifying a number ofcells in which the plurality of orthogonal pilot sequences is not to bereused.

In some implementations of these embodiments, the distributing is basedon the identifying the number of cells.

In some implementations of these embodiments, the distributing enablesthe processing system to identify transmissions of other UEs served bythe plurality of neighboring cells.

In some implementations of these embodiments, the distributing enablesthe processing system to perform channel estimation for the other UEsserved by the plurality of neighboring cells.

In some implementations of these embodiments, the channel estimationenables the processing system to generate null patterns for the otherUEs.

In some implementations of these embodiments, the null patterns are foran uplink (UL) or a downlink (DL).

In some implementations of these embodiments, multi-user(Mu)-multiple-input-multiple-output (MIMO) is employed for a first UE ofthe plurality of UEs based on a first coherence block of the first UEsatisfying a threshold, and single-user (Su)-MIMO is employed for asecond UE of the plurality of UEs based on a second coherence block ofthe second UE not satisfying the threshold.

In some implementations of these embodiments, the determining thecoherence block for each UE of the plurality of UEs comprisesidentifying a coherence time for that UE and a coherence bandwidth forthat UE.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 8F, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In various embodiments, a device may comprise a processing systemincluding a processor, wherein the processing system is communicativelycoupled with a plurality of coherent modular antenna panels. The devicemay further comprise a memory that stores executable instructions that,when executed by the processing system, facilitate performance ofoperations. The operations may include tracking coherence blocks for aplurality of user equipment (UEs) being served by a first cell via theplurality of coherent modular antenna panels. The operations may furtherinclude identifying a particular pilot sequence length based on thetracking the coherence blocks. The operations may further include,responsive to the identifying the particular pilot sequence length,generating a plurality of orthogonal pilot sequences. The operations mayfurther include determining a number of cells in which any given pilotsequence of the plurality of orthogonal pilot sequences is not to bereused. The operations may further include identifying a plurality ofneighboring cells adjacent to the first cell based on the determiningthe number of cells. The operations may further include utilizing, inthe first cell, a first subset of orthogonal pilot sequences of theplurality of orthogonal pilot sequences. The operations may furtherinclude providing, to each neighboring cell of the plurality ofneighboring cells, a respective subset of orthogonal pilot sequences ofa remainder of the plurality of orthogonal pilot sequences.

In some implementations of these embodiments, the providing enables theprocessing system to perform channel estimation for other UEs served bythe plurality of neighboring cells.

In some implementations of these embodiments, the channel estimationenables the processing system to generate null patterns for the otherUEs.

In some implementations of these embodiments, the plurality of coherentmodular antenna panels is operated in both single-user(Su)-multiple-input-multiple-output (MIMO) mode and multi-user (Mu)-MIMOmode.

In some implementations of these embodiments, the tracking the coherenceblocks involves determining coherence times and coherence bandwidths forthe plurality of UEs.

In various embodiments, a method may include determining, by aprocessing system including a processor, a coherence block for each userequipment (UE) of a plurality of UEs being served by a first cell,resulting in a plurality of coherence blocks. The method may furtherinclude, responsive to the determining, identifying, by the processingsystem, a particular coherence block from the plurality of coherenceblocks. The method may further include identifying, by the processingsystem, a sounding reference signal (SRS) sequence length based on theparticular coherence block. The method may further include determining,by the processing system, a plurality of orthogonal SRS sequences basedon the identifying the SRS sequence length. The method may furtherinclude identifying, by the processing system and from the plurality oforthogonal SRS sequences, a first group of orthogonal SRS sequences foruse in the first cell. The method may further include transmitting, bythe processing system and to each neighboring cell of a plurality ofneighboring cells adjacent to the first cell, a respective group oforthogonal SRS sequences from a remainder of the plurality of orthogonalSRS sequences, so as to avoid pilot contamination between the first celland the plurality of neighboring cells.

In some implementations of these embodiments, the determining theplurality of orthogonal SRS sequences comprises determining a pluralityof Zadoff-Chu sequences.

In some implementations of these embodiments, the method may furtherinclude identifying a number of cells in which any given SRS sequence ofthe plurality of orthogonal SRS sequences is not to be reused.

In some implementations of these embodiments, the identifying the firstgroup of orthogonal SRS sequences for use in the first cell, and thetransmitting, are based on the identifying the number of cells.

In some implementations of these embodiments, the transmitting enablesthe processing system to identify transmissions of other UEs served bythe plurality of neighboring cells, perform channel estimation for theother UEs, and generate null patterns for the other UEs.

FIG. 8G depicts an illustrative embodiment of a method 828 in accordancewith various aspects described herein. In some embodiments, one or moreprocess blocks of FIG. 8G can be performed by a RAN or system, such asthe system 162 a. In some embodiments, one or more process blocks ofFIG. 8G may be performed by another device or a group of devicesseparate from or including the system 162 a, such as the network servicemanagement platform 163 a, the RIC 164 a, the CU 174 a, one or more DUs166 a, one or more RUs 168 a, and/or the core network 190.

At 828 a, the method can include receiving, over an uplink (UL) via acombination of coherent modular antenna arrays utilized by a cell, firsttransmissions from a first user equipment (UE) and second transmissionsfrom a second UE, wherein the first UE and the second UE are beingserved by the cell in multi-user (Mu)-multiple-input-multiple-output(MIMO) mode. For example, the system 162 a can, in a manner similar tothat described elsewhere herein, perform one or more operations thatinclude receiving, over an uplink (UL) via a combination of coherentmodular antenna arrays utilized by a cell, first transmissions from afirst user equipment (UE) and second transmissions from a second UE,wherein the first UE and the second UE are being served by the cell inmulti-user (Mu)-multiple-input-multiple-output (MIMO) mode.

At 828 b, the method can include determining, based on the firsttransmissions and the second transmissions, that a probability of thesecond UE interfering with the first UE in the UL satisfies a particularthreshold. For example, the system 162 a can, in a manner similar tothat described elsewhere herein, perform one or more operations thatinclude determining, based on the first transmissions and the secondtransmissions, that a probability of the second UE interfering with thefirst UE in the UL satisfies a particular threshold.

At 828 c, the method can include, responsive to the determining,identifying an adjustment to an UL transmit power for the first UE. Forexample, the system 162 a can, in a manner similar to that describedelsewhere herein, perform one or more operations that include,responsive to the determining, identifying an adjustment to an ULtransmit power for the first UE.

At 828 d, the method can include causing the first UE to implement theadjustment to the UL transmit power. For example, the system 162 a can,in a manner similar to that described elsewhere herein, perform one ormore operations that include causing the first UE to implement theadjustment to the UL transmit power.

In some implementations of these embodiments, the first transmissionsare associated with a first signal strength, the second transmissionsare associated with a second signal strength, and the determining thatthe probability of the second UE interfering with the first UE in the ULsatisfies the particular threshold is based on detecting the secondsignal strength being greater than the first signal strength.

In some implementations of these embodiments, the first transmissionsare associated with a first signal strength, the second transmissionsare associated with a second signal strength, and the determining thatthe probability of the second UE interfering with the first UE in the ULsatisfies the particular threshold is based on detecting that adifference between the first signal strength and the second signalstrength is greater than a certain threshold.

In some implementations of these embodiments, the adjustment comprisesan increase to the UL transmit power for the first UE.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include, responsive to thedetermining, identifying a second adjustment to a second UL transmitpower for the second UE.

In some implementations of these embodiments, the second adjustmentcomprises a decrease to the second UL transmit power for the second UE.

In some implementations of these embodiments, the first UE is located ator proximate to an edge of the cell, and the second UE is located closerto the combination of coherent modular antenna arrays than the first UE.

In some implementations of these embodiments, communications between thecombination of coherent modular antenna arrays and the first and secondUEs are in frequency division duplex (FDD).

In some implementations of these embodiments, communications between thecombination of coherent modular antenna arrays and the first and secondUEs are in time division duplex (TDD).

In some implementations of these embodiments, the combination ofcoherent modular antenna arrays is operated in single-user (Su)-MIMOmode for one or more other UEs.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 8G, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In various embodiments, a device may comprise a processing systemincluding a processor, wherein the processing system is communicativelycoupled with a plurality of coherent modular antenna panels. The devicemay further comprise a memory that stores executable instructions that,when executed by the processing system, facilitate performance ofoperations. The operations may include receiving, over an uplink (UL)via the plurality of coherent modular antenna panels, first signals froma first user equipment (UE) and second signals from a second UE, whereinthe first UE and the second UE are being served in multi-user(Mu)-multiple-input-multiple-output (MIMO) mode. The operations mayfurther include identifying, based on the first signals and the secondsignals, that a probability of the second UE interfering with the firstUE in the UL satisfies a particular threshold. The operations mayfurther include, based on the identifying, determining an adjustment toan UL transmit power for the second UE. The operations may furtherinclude causing the second UE to implement the adjustment to the ULtransmit power.

In some implementations of these embodiments, the first signals areassociated with a first signal strength, the second signals areassociated with a second signal strength, and the identifying that theprobability of the second UE interfering with the first UE in the ULsatisfies the particular threshold is based on detecting the secondsignal strength being greater than the first signal strength.

In some implementations of these embodiments, the first signals areassociated with a first signal strength, the second signals areassociated with a second signal strength, and the identifying that theprobability of the second UE interfering with the first UE in the ULsatisfies the particular threshold is based on detecting that adifference between the first signal strength and the second signalstrength is greater than a certain threshold.

In some implementations of these embodiments, the adjustment comprises adecrease to the UL transmit power for the second UE.

In some implementations of these embodiments, the operations may furtherinclude, responsive to the identifying, determining a differentadjustment to an UL transmit power for the first UE, where the differentadjustment comprises an increase to the UL transmit power for the firstUE.

In various embodiments, a method may include receiving, by a processingsystem associated with an aggregation of coherent modular antenna arraysand including a processor, first signals from a first user equipment(UE) and second signals from a second UE, wherein the first UE and thesecond UE are being served in multi-user(Mu)-multiple-input-multiple-output (MIMO) mode. The method may furtherinclude identifying, by the processing system, based on the firstsignals and the second signals, that the second UE is likely tointerfere with the first UE in an uplink (UL). The method may furtherinclude, based on the identifying, determining, by the processingsystem, a first adjustment to a first UL transmit power for the first UEand a second adjustment to a second UL transmit power for the second UE.The method may further include causing, by the processing system, thefirst UE to implement the first adjustment to the first UL transmitpower and the second UE to implement the second adjustment to the secondUL transmit power.

In some implementations of these embodiments, the first signals areassociated with a first signal strength, the second signals areassociated with a second signal strength, and the identifying that thesecond UE is likely to interfere with the first UE in the UL is based ondetecting the second signal strength being greater than the first signalstrength.

In some implementations of these embodiments, the first signals areassociated with a first signal strength, the second signals areassociated with a second signal strength, and the identifying that thesecond UE is likely to interfere with the first UE in the UL is based ondetecting that a difference between the first signal strength and thesecond signal strength is greater than a threshold.

In some implementations of these embodiments, the causing involvescontrolling UL transmit power parameters via an Open Radio AccessNetwork (O-RAN) interface.

In some implementations of these embodiments, the aggregation ofcoherent modular antenna arrays is operated in single-user (Su)-MIMOmode for one or more other UEs.

FIG. 8H depicts an illustrative embodiment of a method 830 in accordancewith various aspects described herein. In some embodiments, one or moreprocess blocks of FIG. 8H can be performed by a RAN or system, such asthe system 162 a. In some embodiments, one or more process blocks ofFIG. 8H may be performed by another device or a group of devicesseparate from or including the system 162 a, such as the network servicemanagement platform 163 a, the RIC 164 a, the CU 174 a, one or more DUs166 a, one or more RUs 168 a, and/or the core network 190.

At 830 a, the method can include obtaining channel cross correlationdata relating to multiple user equipment (UEs) being served in a cell,wherein the channel cross correlation data comprises a correlationcoefficient associated with a first UE of the multiple UEs and a secondUE of the multiple UEs. For example, the system 162 a can, in a mannersimilar to that described elsewhere herein, perform one or moreoperations that include obtaining channel cross correlation datarelating to multiple user equipment (UEs) being served in a cell,wherein the channel cross correlation data comprises a correlationcoefficient associated with a first UE of the multiple UEs and a secondUE of the multiple UEs.

At 830 b, the method can include identifying that the first UE isexperiencing decreasing throughput. For example, the system 162 a can,in a manner similar to that described elsewhere herein, perform one ormore operations that include identifying that the first UE isexperiencing decreasing throughput.

At 830 c, the method can include, responsive to the identifying that thefirst UE is experiencing decreasing throughput, determining whether thecorrelation coefficient associated with the first UE and the second UEsatisfies a correlation threshold. For example, the system 162 a can, ina manner similar to that described elsewhere herein, perform one or moreoperations that include, responsive to the identifying that the first UEis experiencing decreasing throughput, determining whether thecorrelation coefficient associated with the first UE and the second UEsatisfies a correlation threshold.

At 830 d, the method can include, based on a first determination thatthe correlation coefficient does not satisfy the correlation threshold,adjusting a downlink (DL) transmit power allocation for transmissionsdirected to the first UE. For example, the system 162 a can, in a mannersimilar to that described elsewhere herein, perform one or moreoperations that include, based on a first determination that thecorrelation coefficient does not satisfy the correlation threshold,adjusting a downlink (DL) transmit power allocation for transmissionsdirected to the first UE.

In some implementations of these embodiments, the determining whetherthe correlation coefficient satisfies the correlation thresholdcomprises determining whether the correlation coefficient is less thanor equal to the correlation threshold.

In some implementations of these embodiments, the adjusting the DLtransmit power allocation comprises decreasing the DL transmit powerallocation.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include, based on a seconddetermination that the correlation coefficient satisfies the correlationthreshold, maintaining the DL transmit power allocation.

In some implementations of these embodiments, the first UE is located ator proximate to an edge of the cell.

In some implementations of these embodiments, the multiple UEs are beingserved in the cell in multi-user (Mu)-multiple-input-multiple-output(MIMO) mode.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include identifying that athroughput demand of the second UE satisfies a throughput threshold,where the determining whether the correlation coefficient satisfies thecorrelation threshold is further responsive to the identifying that thethroughput demand of the second UE satisfies the throughput threshold.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include identifying that a signalassociated with the second UE satisfies a signal quality threshold,where the determining whether the correlation coefficient satisfies thecorrelation threshold is further responsive to the identifying that thesignal associated with the second UE satisfies the signal qualitythreshold.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include monitoring a throughput ofthe first UE, where the identifying that the first UE is experiencingdecreasing throughput is based on the monitoring the throughput of thefirst UE.

In some implementations of these embodiments, the channel crosscorrelation data comprises respective channel estimations for themultiple UEs.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 8H, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In various embodiments, a device may comprise a processing systemincluding a processor, wherein the processing system is communicativelycoupled with a plurality of coherent modular antenna panels. The devicemay further comprise a memory that stores executable instructions that,when executed by the processing system, facilitate performance ofoperations. The operations may include determining channel crosscorrelation information relating to a plurality of user equipment (UEs),wherein the channel cross correlation information comprises respectivechannel estimations for the plurality of UEs, and wherein the channelcross correlation information is normalized with a correlationcoefficient associated with a first UE of the plurality of UEs and asecond UE of the plurality of UEs. The operations may further includeidentifying that a throughput of the first UE satisfies a condition. Theoperations may further include, based on the identifying that thethroughput of the first UE satisfies the condition, determining whetherthe correlation coefficient associated with the first UE and the secondUE satisfies a correlation threshold. The operations may furtherinclude, responsive to determining that the correlation coefficient doesnot satisfy the correlation threshold, adjusting a downlink (DL)transmit power allocation for transmissions directed to the second UE.

In some implementations of these embodiments, the adjusting the DLtransmit power allocation comprises increasing the DL transmit powerallocation for the transmissions directed to the second UE.

In some implementations of these embodiments, the operations may furtherinclude, based on determining that the correlation coefficient satisfiesthe correlation threshold, maintaining the DL transmit power allocationfor the transmissions directed to the second UE.

In some implementations of these embodiments, the plurality of UEs isbeing served in multi-user (Mu)-multiple-input-multiple-output (MIMO)mode.

In some implementations of these embodiments, each modular antenna panelof the plurality of coherent modular antenna panels comprises arespective group of antenna elements, resulting in a multiple groups ofantenna elements, where the plurality of UEs is being served in Mu-MIMOmode via the multiple groups of antenna elements.

In various embodiments, a method may include receiving, by a processingsystem including a processor, channel cross correlation data relating tomultiple user equipment (UEs) being served in a cell, wherein thechannel cross correlation data comprises a correlation coefficientassociated with a first UE of the multiple UEs and a second UE of themultiple UEs. The method may further include identifying, by theprocessing system, that a throughput of the first UE is less than aparticular threshold and is located at or proximate to an edge of thecell. The method may further include, responsive to the identifying thatthe throughput of the first UE is less than the particular threshold andis located at or proximate to the edge of the cell, determining, by theprocessing system, whether the correlation coefficient associated withthe first UE and the second UE satisfies a correlation threshold. Themethod may further include, based on a first determination that thecorrelation coefficient does not satisfy the correlation threshold,adjusting, by the processing system, a first downlink (DL) transmitpower allocation for first transmissions directed to the first UE,adjusting, by the processing system, a second DL transmit powerallocation for second transmissions directed to the second UE, or acombination thereof.

In some implementations of these embodiments, the determining whetherthe correlation coefficient satisfies the correlation thresholdcomprises determining whether the correlation coefficient is less thanor equal to the correlation threshold.

In some implementations of these embodiments, the adjusting the first DLtransmit power allocation comprises decreasing the first DL transmitpower allocation, where the adjusting the second DL transmit powerallocation comprises increasing the second DL transmit power allocation.

In some implementations of these embodiments, the method may furtherinclude, based on a second determination that the correlationcoefficient satisfies the correlation threshold, maintaining the firstDL transmit power allocation, maintaining the second DL transmit powerallocation, or a combination thereof.

In some implementations of these embodiments, the multiple UEs are beingserved in the cell in multi-user (Mu)-multiple-input-multiple-output(MIMO) mode.

FIG. 8J depicts an illustrative embodiment of a method 832 in accordancewith various aspects described herein. In some embodiments, one or moreprocess blocks of FIG. 8J can be performed by a RAN or system, such asthe system 162 a. In some embodiments, one or more process blocks ofFIG. 8J may be performed by another device or a group of devicesseparate from or including the system 162 a, such as the network servicemanagement platform 163 a, the RIC 164 a, the CU 174 a, one or more DUs166 a, one or more RUs 168 a, and/or the core network 190.

At 832 a, the method can include obtaining spatial separability data formultiple user equipment (UEs) being served in a cell, wherein thespatial separability data relates to a correlation coefficientassociated with a first UE of the multiple UEs and a second UE of themultiple UEs. For example, the system 162 a can, in a manner similar tothat described elsewhere herein, perform one or more operations thatinclude obtaining spatial separability data that identifies channelcross correlation for multiple user equipment (UEs) being served in acell, wherein the channel cross correlation relates to a correlationcoefficient associated with a first UE of the multiple UEs and a secondUE of the multiple UEs.

At 832 b, the method can include identifying that the first UE isexperiencing decreasing throughput. For example, the system 162 a can,in a manner similar to that described elsewhere herein, perform one ormore operations that include identifying that the first UE isexperiencing decreasing throughput.

At 832 c, the method can include, responsive to the identifying that thefirst UE is experiencing decreasing throughput, determining whether thecorrelation coefficient associated with the first UE and the second UEsatisfies a correlation threshold. For example, the system 162 a can, ina manner similar to that described elsewhere herein, perform one or moreoperations that include, responsive to the identifying that the first UEis experiencing decreasing throughput, determining whether thecorrelation coefficient associated with the first UE and the second UEsatisfies a correlation threshold.

At 832 d, the method can include, based on a first determination thatthe correlation coefficient satisfies the correlation threshold, causinga scheduling priority for the first UE to be increased. For example, thesystem 162 a can, in a manner similar to that described elsewhereherein, perform one or more operations that include, based on a firstdetermination that the correlation coefficient satisfies the correlationthreshold, causing a scheduling priority for the first UE to beincreased.

In some implementations of these embodiments, the determining whetherthe correlation coefficient satisfies the correlation thresholdcomprises determining whether the correlation coefficient is less thanor equal to the correlation threshold.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include, based on a seconddetermination that the correlation coefficient does not satisfy thecorrelation threshold, preventing the scheduling priority for the firstUE from being increased.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include, based on a seconddetermination that the correlation coefficient does not satisfy thecorrelation threshold, causing scheduling for the first UE to becomede-prioritized.

In some implementations of these embodiments, the first UE is located ator within a threshold distance from an edge of the cell.

In some implementations of these embodiments, the multiple UEs are beingserved in the cell in multi-user (Mu)-multiple-input-multiple-output(MIMO) mode.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include identifying that athroughput demand of the second UE satisfies a throughput threshold,where the determining whether the correlation coefficient satisfies thecorrelation threshold is further responsive to the identifying that thethroughput demand of the second UE satisfies the throughput threshold.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include identifying that a signalassociated with the second UE satisfies a signal quality threshold,where the determining whether the correlation coefficient satisfies thecorrelation threshold is further responsive to the identifying that thesignal associated with the second UE satisfies the signal qualitythreshold.

In some implementations of these embodiments, the spatial separabilitydata comprises channel cross correlation that relates to channelestimations for the multiple UEs.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include increasing a number of UEsfor simultaneous scheduling based on the spatial separability datasatisfying a particular threshold.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 8J, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In various embodiments, a device may comprise a processing systemincluding a processor, wherein the processing system is communicativelycoupled with a plurality of coherent modular antenna panels. The devicemay further comprise a memory that stores executable instructions that,when executed by the processing system, facilitate performance ofoperations. The operations may include determining channel crosscorrelation information relating to a plurality of user equipment (UEs),wherein the channel cross correlation information comprises respectivechannel estimations for the plurality of UEs, and wherein the channelcross correlation information is normalized with a correlationcoefficient associated with a first UE of the plurality of UEs and asecond UE of the plurality of UEs. The operations may further includeidentifying that a throughput of the first UE satisfies a condition. Theoperations may further include, based on the identifying that thethroughput of the first UE satisfies the condition, determining whetherthe correlation coefficient associated with the first UE and the secondUE satisfies a correlation threshold. The operations may furtherinclude, responsive to determining that the correlation coefficient doesnot satisfy the correlation threshold, preventing a scheduling priorityfor the first UE from being increased or causing scheduling for thefirst UE to become de-prioritized.

In some implementations of these embodiments, the determining whetherthe correlation coefficient satisfies the correlation thresholdcomprises determining whether the correlation coefficient is less thanor equal to the correlation threshold.

In some implementations of these embodiments, the plurality of UEs isbeing served in multi-user (Mu)-multiple-input-multiple-output (MIMO)mode.

In some implementations of these embodiments, each modular antenna panelof the plurality of coherent modular antenna panels comprises arespective group of antenna elements, resulting in a multiple groups ofantenna elements, where the plurality of UEs is being served in Mu-MIMOmode via the multiple groups of antenna elements.

In some implementations of these embodiments, the operations may furtherinclude monitoring a throughput of the first UE, and wherein theidentifying that the throughput of the first UE satisfies the conditionis based on the monitoring the throughput of the first UE.

In various embodiments, a method may include receiving, by a processingsystem including a processor, channel cross correlation data relating tomultiple user equipment (UEs) being served in a cell, wherein thechannel cross correlation data comprises a correlation coefficientassociated with a first UE of the multiple UEs and a second UE of themultiple UEs. The method may further include identifying, by theprocessing system, that a throughput of the first UE is less than aparticular threshold and is located at or within a threshold distancefrom an edge of the cell. The method may further include, responsive tothe identifying that the throughput of the first UE is less than theparticular threshold and is located at or within the threshold distancefrom the edge of the cell, determining, by the processing system,whether the correlation coefficient associated with the first UE and thesecond UE satisfies a correlation threshold. The method may furtherinclude, based on a first determination that the correlation coefficientsatisfies the correlation threshold, permitting, by the processingsystem, a scheduling priority for the first UE to be increased.

In some implementations of these embodiments, the determining whetherthe correlation coefficient satisfies the correlation thresholdcomprises determining whether the correlation coefficient is less thanor equal to the correlation threshold.

In some implementations of these embodiments, the method may furtherinclude, based on a second determination that the correlationcoefficient does not satisfy the correlation threshold, preventing thescheduling priority for the first UE from being increased.

In some implementations of these embodiments, the method may furtherinclude, based on a second determination that the correlationcoefficient does not satisfy the correlation threshold, causingscheduling for the first UE to become de-prioritized.

In some implementations of these embodiments, the multiple UEs are beingserved in the cell in multi-user (Mu)-multiple-input-multiple-output(MIMO) mode.

FIG. 8K depicts an illustrative embodiment of a method 834 in accordancewith various aspects described herein. In some embodiments, one or moreprocess blocks of FIG. 8K can be performed by a RAN or system, such asthe system 162 a. In some embodiments, one or more process blocks ofFIG. 8K may be performed by another device or a group of devicesseparate from or including the system 162 a, such as the network servicemanagement platform 163 a, the RIC 164 a, the CU 174 a, one or more DUs166 a, one or more RUs 168 a, and/or the core network 190.

At 834 a, the method can include identifying a coherence time associatedwith a user equipment (UE) and a coherence bandwidth associated with theUE. For example, the system 162 a can, in a manner similar to thatdescribed elsewhere herein, perform one or more operations that includeidentifying a coherence time associated with a user equipment (UE) and acoherence bandwidth associated with the UE.

At 834 b, the method can include determining a coherence block based onthe coherence time and the coherence bandwidth. For example, the system162 a can, in a manner similar to that described elsewhere herein,perform one or more operations that include determining a coherenceblock based on the coherence time and the coherence bandwidth. At 834 c,the method can include determining whether the coherence block satisfiesa threshold. For example, the system 162 a can, in a manner similar tothat described elsewhere herein, perform one or more operations thatinclude determining whether the coherence block satisfies a threshold.

At 834 d, the method can include, based on a first determination thatthe coherence block satisfies the threshold, employing a plurality ofmodular antenna arrays, operating as a coherent antenna system, inmulti-user (Mu)-multiple-input-multiple-output (MIMO) for the UE. Forexample, the system 162 a can, in a manner similar to that describedelsewhere herein, perform one or more operations that include, based ona first determination that the coherence block satisfies the threshold,employing a plurality of modular antenna arrays, operating as a coherentantenna system, in multi-user (Mu)-multiple-input-multiple-output (MIMO)for the UE.

At 834 e, the method can include, based on a second determination thatthe coherence block does not satisfy the threshold, employing theplurality of modular antenna arrays in single-user (Su)-MIMO for the UE.For example, the system 162 a can, in a manner similar to that describedelsewhere herein, perform one or more operations that include, based ona second determination that the coherence block does not satisfy thethreshold, employing the plurality of modular antenna arrays insingle-user (Su)-MIMO for the UE.

In some implementations of these embodiments, the determining thecoherence block comprises obtaining a product of the coherence time andthe coherence bandwidth.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include tracking the coherence blockafter the employing the plurality of modular antenna arrays in Mu-MIMOfor the UE, determining, based on the tracking the coherence block, thatthe coherence block does not satisfy the threshold, and responsive tothe determining that the coherence block does not satisfy the threshold,employing the plurality of modular antenna arrays in Su-MIMO for the UE.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include tracking the coherence blockafter the employing the plurality of modular antenna arrays in Su-MIMOfor the UE, determining, based on the tracking the coherence block, thatthe coherence block satisfies the threshold, and responsive to thedetermining that the coherence block satisfies the threshold, employingthe plurality of modular antenna arrays in Mu-MIMO for the UE.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include, based on the firstdetermination that the coherence block satisfies the threshold,performing an average of sounding reference signal (SRS) data obtainedfrom the UE to maximize uplink (UL) coverage for the UE.

In some implementations of these embodiments, the threshold comprises anumber of sounding reference signal (SRS) symbols.

In some implementations of these embodiments, the employing theplurality of modular antenna arrays in Mu-MIMO for the UE is transparentto other UEs for which Su-MIMO is employed.

In some implementations of these embodiments, the identifying thecoherence time and the coherence bandwidth comprises obtaining thecoherence time and the coherence bandwidth via an Open Radio AccessNetwork (O-RAN) compliant interface.

In some implementations of these embodiments, the plurality of modularantenna arrays operates in a Mid-band spectrum.

In some implementations of these embodiments, the plurality of modularantenna arrays operates in time division duplex (TDD), frequencydivision duplex (FDD), or a combination thereof.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 8K, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In various embodiments, a non-transitory machine-readable medium maycomprise executable instructions that, when executed by a processingsystem communicatively coupled with a combination of modular antennapanels and including a processor, facilitate performance of operations.The operations may include determining a coherence time and a coherencebandwidth for a user equipment (UE), wherein the UE is located at orwithin a threshold distance from a cell edge. The operations may furtherinclude tracking a coherence block based on the coherence time and thecoherence bandwidth. The operations may further include determiningwhether the coherence block is larger than a predefined coherence blocksize. The operations may further include, based on a first determinationthat the coherence block is larger than the predefined coherence blocksize, employing the combination of modular antenna panels in multi-user(Mu)-multiple-input-multiple-output (MIMO) for the UE. The operationsmay further include, based on a second determination that the coherenceblock is not larger than the predefined coherence block size, employingthe combination of modular antenna panels in single-user (Su)-MIMO forthe UE.

In some implementations of these embodiments, the operations may furtherinclude monitoring the coherence block after the employing thecombination of modular antenna panels in Mu-MIMO for the UE,determining, based on the monitoring the coherence block, that thecoherence block is no longer larger than the predefined coherence blocksize, and responsive to the determining that the coherence block is nolonger larger than the predefined coherence block size, employing thecombination of modular antenna panels in Su-MIMO for the UE.

In some implementations of these embodiments, the operations may furtherinclude, based on the first determination that the coherence block islarger than the predefined coherence block size, performing an averageof sounding reference signal (SRS) data obtained from the UE to maximizeuplink (UL) coverage for the UE.

In some implementations of these embodiments, the employing thecombination of modular antenna panels in Mu-MIMO for the UE istransparent to other UEs for which Su-MIMO is employed.

In some implementations of these embodiments, the combination of modularantenna panels operates in time division duplex (TDD), frequencydivision duplex (FDD), or a combination thereof.

In various embodiments, a method may include tracking, by a processingsystem, a coherence block for a user equipment (UE). The method mayfurther include determining, by the processing system and based on thetracking, whether to serve the UE in multi-user(Mu)-multiple-input-multiple-output (MIMO) mode or single-user (Su)-MIMOmode. The method may further include, based on a first determination toserve the UE in the Mu-MIMO mode, employing, by the processing system,an aggregation of coherent modular antenna arrays to operate in Mu-MIMOfor the UE. The method may further include, based on a seconddetermination to serve the UE in the Su-MIMO mode, employing, by theprocessing system, the aggregation of coherent modular antenna arrays tooperate in Su-MIMO for the UE.

In some implementations of these embodiments, the coherence block isbased on a coherence time and a coherence bandwidth for the UE.

In some implementations of these embodiments, the method may furtherinclude monitoring the coherence block after the employing theaggregation of coherent modular antenna arrays to operate in Mu-MIMO forthe UE, determining, based on the monitoring the coherence block, toserve the UE in the Su-MIMO mode, and responsive to the determining toserve the UE in the Su-MIMO mode, employing the aggregation of coherentmodular antenna arrays in Su-MIMO for the UE.

In some implementations of these embodiments, the method may furtherinclude, based on the first determination, performing an average ofsounding reference signal (SRS) data obtained from the UE to maximizeuplink (UL) coverage for the UE.

In some implementations of these embodiments, the employing theaggregation of coherent modular antenna arrays to operate in Mu-MIMO forthe UE is transparent to other UEs for which Su-MIMO is employed.

FIG. 8L depicts an illustrative embodiment of a method 836 in accordancewith various aspects described herein. In some embodiments, one or moreprocess blocks of FIG. 8L can be performed by a RAN or system, such asthe system 162 a. In some embodiments, one or more process blocks ofFIG. 8L may be performed by another device or a group of devicesseparate from or including the system 162 a, such as the network servicemanagement platform 163 a, the RIC 164 a, the CU 174 a, one or more DUs166 a, one or more RUs 168 a, and/or the core network 190.

At 836 a, the method can include obtaining a plurality of pilot signalsprovided by a plurality of user equipment (UEs), wherein each pilotsignal of the plurality of pilot signals is provided by a respective UEof the plurality of UEs. For example, the system 162 a can, in a mannersimilar to that described elsewhere herein, perform one or moreoperations that include obtaining a plurality of pilot signals providedby a plurality of user equipment (UEs), wherein each pilot signal of theplurality of pilot signals is provided by a respective UE of theplurality of UEs.

At 836 b, the method can include estimating an uplink (UL) channel basedon the plurality of pilot signals. For example, the system 162 a can, ina manner similar to that described elsewhere herein, perform one or moreoperations that include estimating an uplink (UL) channel based on theplurality of pilot signals.

At 836 c, the method can include calculating a plurality of UL combiningweights for the aggregation of modular antenna arrays responsive to theestimating the UL channel. For example, the system 162 a can, in amanner similar to that described elsewhere herein, perform one or moreoperations that include calculating a plurality of UL combining weightsfor the aggregation of modular antenna arrays responsive to theestimating the UL channel.

At 836 d, the method can include predicting a downlink (DL) channelbased on spatial correlations relating to the plurality of UEs,resulting in a predicted DL channel, wherein the spatial correlationsare derived from channel vectors associated with the plurality of UEs.For example, the system 162 a can, in a manner similar to that describedelsewhere herein, perform one or more operations that include predictinga downlink (DL) channel based on spatial correlations relating to theplurality of UEs, resulting in a predicted DL channel, wherein thespatial correlations are derived from channel vectors associated withthe plurality of UEs.

At 836 e, the method can include calculating a plurality of DL precodingweights for the aggregation of modular antenna arrays based on thepredicted DL channel. For example, the system 162 a can, in a mannersimilar to that described elsewhere herein, perform one or moreoperations that include calculating a plurality of DL precoding weightsfor the aggregation of modular antenna arrays based on the predicted DLchannel.

At 836 f, the method can include utilizing the plurality of UL combiningweights and the plurality of DL precoding weights to operate theaggregation of modular antenna arrays for the plurality of UEs. Forexample, the system 162 a can, in a manner similar to that describedelsewhere herein, perform one or more operations that include utilizingthe plurality of UL combining weights and the plurality of DL precodingweights to operate the aggregation of modular antenna arrays for theplurality of UEs.

In some implementations of these embodiments, the UL channel and the DLchannel are operated in frequency division duplex (FDD).

In some implementations of these embodiments, the utilizing enablesmulti-user (Mu)-multiple-input-multiple-output (MIMO) to be employed forone or more UEs of the plurality of UEs.

In some implementations of these embodiments, the predicting the DLchannel based on the spatial correlations relating to the plurality ofUEs comprises performing one or more averages of the spatialcorrelations.

In some implementations of these embodiments, the plurality of ULcombining weights comprises a plurality of amplitudes for the UL, aplurality of phases for the UL, or a combination thereof.

In some implementations of these embodiments, the plurality of DLprecoding weights comprises a plurality of amplitudes for the DL, aplurality of phases for the DL, or a combination thereof.

In some implementations of these embodiments, each pilot signal of theplurality of pilot signals comprises a sounding reference signal (SRS),where, for input interference relating to a neighbor cell UE, a fixedUE, or an active external source, the calculating the plurality of ULcombining weights and the calculating the plurality of DL precodingweights involves subtracting a summation of known UE channels from anautocovariance determined based on received signals in order to identifyor isolate the input interference.

In some implementations of these embodiments, the aggregation of antennaarrays comprises a plurality of antenna panels, where each antenna panelof the plurality of antenna panels comprises a plurality of antennaelements.

In some implementations of these embodiments, each UL combining weightof the plurality of UL combining weights corresponds to a respectiveantenna element of the plurality of antenna elements.

In some implementations of these embodiments, each DL precoding weightof the plurality of DL precoding weights corresponds to a respectiveantenna element of the plurality of antenna elements.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 8L, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In various embodiments, a device may comprise a processing systemincluding a processor, wherein the processing system is communicativelycoupled with a plurality of coherent modular antenna panels. The devicemay further comprise a memory that stores executable instructions that,when executed by the processing system, facilitate performance ofoperations. The operations may include receiving a plurality oforthogonal sounding reference signals (SRS) from a plurality of userequipment (UEs), wherein each SRS of the plurality of orthogonal SRS isprovided by a respective UE of the plurality of UEs. The operations mayfurther include determining a downlink (DL) channel, in frequencydivision duplex (FDD), using spatial correlations relating to theplurality of UEs, wherein the spatial correlations are based on theplurality of orthogonal SRS. The operations may further includecalculating a plurality of DL precoding weights for the plurality ofcoherent modular antenna panels responsive to the determining the DLchannel. The operations may further include applying the plurality of DLprecoding weights to the plurality of coherent modular antenna panels toenable multi-user (Mu)-multiple-input-multiple-output (MIMO) for theplurality of UEs.

In some implementations of these embodiments, the determining the DLchannel using the spatial correlations relating to the plurality of UEscomprises determining one or more expectations of the spatialcorrelations.

In some implementations of these embodiments, each modular antenna panelof the plurality of coherent modular antenna panels comprises aplurality of antenna elements, resulting in multiple pluralities ofantenna elements.

In some implementations of these embodiments, each DL precoding weightof the plurality of DL precoding weights corresponds to a respectiveantenna element of the multiple pluralities of antenna elements.

In some implementations of these embodiments, the plurality of UEsincludes fixed wireless customer premises equipment (CPEs) located at orwithin a threshold distance from a cell edge associated with theprocessing system.

In various embodiments, a non-transitory machine-readable medium,comprising executable instructions that, when executed by a processingsystem that is associated with a coherent combination of modular antennaarrays and that includes a processor, facilitate performance ofoperations. The operations may include estimating a frequency divisionduplex (FDD) uplink (UL) channel based on pilot signals transmitted by aplurality of user equipment (UEs). The operations may further include,responsive to the estimating the FDD UL channel, determining a pluralityof UL weights for the coherent combination of modular antenna arrays.The operations may further include predicting an FDD downlink (DL)channel based on spatial correlations corresponding to the plurality ofUEs, resulting in a predicted FDD DL channel, wherein the spatialcorrelations are based on channel vectors associated with the pluralityof UEs. The operations may further include determining a plurality of DLweights for the coherent combination of modular antenna arrays based onthe predicted FDD DL channel. The operations may further include causingthe plurality of UL weights and the plurality of DL weights to beapplied to the coherent combination of modular antenna arrays for theplurality of UEs.

In some implementations of these embodiments, the causing enablesmulti-user (Mu)-multiple-input-multiple-output (MIMO) to be employed forone or more UEs of the plurality of UEs.

In some implementations of these embodiments, the predicting the FDD DLchannel based on the spatial correlations corresponding to the pluralityof UEs comprises performing one or more averages of the spatialcorrelations, where the predicting the FDD DL channel further involvescompensating for a difference between an UL frequency and a DL frequencyby performing spatial-scaling of UL channel data such that a first phasedifference between DL signals emitted by different antenna elements ofthe coherent combination of modular antenna arrays is the same as asecond phase difference in an UL direction.

In some implementations of these embodiments, the pilot signals comprisesounding reference signals (SRS).

In some implementations of these embodiments, each modular antenna arrayof the coherent combination of modular antenna arrays comprises a groupof antenna elements, resulting in multiple groups of antenna elements,where each antenna element of the multiple groups of antenna elements isassociated with a respective programmable device.

FIG. 8M depicts an illustrative embodiment of a method 838 in accordancewith various aspects described herein. In some embodiments, one or moreprocess blocks of FIG. 8M can be performed by a RAN or system, such asthe system 162 a. In some embodiments, one or more process blocks ofFIG. 8M may be performed by another device or a group of devicesseparate from or including the system 162 a, such as the network servicemanagement platform 163 a, the RIC 164 a, the CU 174 a, one or more DUs166 a, one or more RUs 168 a, and/or the core network 190.

At 838 a, the method can include receiving, via a combination ofcoherent modular antenna panels, pilot signals from a user equipment(UE) in single-user (Su)-multiple-input-multiple-output (MIMO) mode,wherein each modular antenna panel of the combination of coherentmodular antenna panels comprises a group of antenna elements, resultingin multiple groups of antenna elements. For example, the system 162 acan, in a manner similar to that described elsewhere herein, perform oneor more operations that include receiving, via a combination of coherentmodular antenna panels, pilot signals from a user equipment (UE) insingle-user (Su)-multiple-input-multiple-output (MIMO) mode, whereineach modular antenna panel of the combination of coherent modularantenna panels comprises a group of antenna elements, resulting inmultiple groups of antenna elements.

At 838 b, the method can include estimating an UL channel for the UE anda DL channel for the UE using channel vectors derived from the pilotsignals, resulting in reciprocity-based channel estimation, wherein theestimating the DL channel using the channel vectors derived from thepilot signals reduces or eliminates a need to transmit, using eachantenna element in the multiple groups of antenna elements, DL pilotsignals for DL channel estimation, thereby reducing or eliminatingoverhead for the UE. For example, the system 162 a can, in a mannersimilar to that described elsewhere herein, perform one or moreoperations that include estimating an UL channel for the UE and a DLchannel for the UE using channel vectors derived from the pilot signals,resulting in reciprocity-based channel estimation, wherein theestimating the DL channel using the channel vectors derived from thepilot signals reduces or eliminates a need to transmit, using eachantenna element in the multiple groups of antenna elements, DL pilotsignals for DL channel estimation, thereby reducing or eliminatingoverhead for the UE.

In some implementations of these embodiments, communications between thecombination of coherent modular antenna panels and the UE are in timedivision duplex (TDD).

In some implementations of these embodiments, the pilot signals comprisesounding reference signals (SRS).

In some implementations of these embodiments, the DL pilot signalscomprise channel state information (CSI)-reference signals (RS).

In some implementations of these embodiments, the combination ofcoherent modular antenna panels transparently serves the UE in Su-MIMOmode and other UEs in multi-user (Mu)-MIMO mode.

In some implementations of these embodiments, a number of antennaelements in the multiple groups of antenna elements is greater than anumber of antennas of the UE.

In some implementations of these embodiments, the combination ofcoherent modular antenna panels serves the UE in Su-MIMO mode based ondetecting that a coherence block associated with the UE is smaller thana threshold size.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 8M, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In various embodiments, a non-transitory machine-readable medium maycomprise executable instructions that, when executed by a processingsystem communicatively coupled with an aggregation of coherent modularantenna panels and including a processor, facilitate performance ofoperations. The operations may include receiving, via the aggregation ofcoherent modular antenna panels, sounding reference signals (SRS) from auser equipment (UE) in single-user (Su)-multiple-input-multiple-output(MIMO) mode in frequency division duplex (FDD), wherein each modularantenna panel of the aggregation of coherent modular antenna panelscomprises a set of antenna elements, resulting in multiple sets ofantenna elements. The operations may further include estimating a DLchannel for the UE using channel vectors derived from the SRS, resultingin reciprocity-based channel estimation, wherein the estimating involvesaveraging of spatial correlations relating to the channel vectors, andwherein the estimating reduces or eliminates a need to transmit, usingeach antenna element in the multiple sets of antenna elements, channelstate information (CSI)-reference signals (RS) for DL channelestimation, thereby reducing or eliminating overhead for the UE.

In some implementations of these embodiments, the aggregation ofcoherent modular antenna panels transparently serves the UE in Su-MIMOmode and other UEs in multi-user (Mu)-MIMO mode.

In some implementations of these embodiments, a number of antennaelements in the multiple sets of antenna elements is greater than anumber of ports or antennas of the UE.

In some implementations of these embodiments, the aggregation ofcoherent modular antenna panels serves the UE in Su-MIMO mode based ondetecting that a coherence block associated with the UE is smaller thana threshold size.

In some implementations of these embodiments, the processing system isimplemented in an Open Radio Access Network (O-RAN) architecture.

In various embodiments, a method may include receiving, by a processingsystem including a processor, and via a combination of coherent modularantenna arrays, sounding reference signals (SRS) from a user equipment(UE) in single-user (Su)-multiple-input-multiple-output (MIMO) mode infrequency division duplex (FDD), wherein each modular antenna array ofthe combination of coherent modular antenna arrays comprises a group ofantenna elements, resulting in multiple groups of antenna elements. Themethod may further include estimating, by the processing system, anuplink (UL) channel for the UE using channel vectors derived from theSRS. The method may further include performing, by the processingsystem, a logical partition of the combination of coherent modularantenna arrays, resulting in a first partition of the combination ofcoherent modular antenna arrays and a second partition of thecombination of coherent modular antenna arrays. The method may furtherinclude identifying, by the processing system, a first antenna elementfrom the first partition and a second antenna element from the secondpartition, wherein the first antenna element comprises a firstpolarization and a second polarization, and wherein the second antennaelement comprises a third polarization and a fourth polarization. Themethod may further include causing, by the processing system, the firstantenna element to transmit a first orthogonal pilot signal at the firstpolarization and to transmit a second orthogonal pilot signal at thesecond polarization, and causing the second antenna element to transmita third orthogonal pilot signal at the third polarization and totransmit a fourth orthogonal pilot signal at the fourth polarization,resulting in four orthogonal pilot signals being transmitted. The methodmay further include, responsive to the causing, obtaining, by theprocessing system, feedback from the UE via the combination of coherentmodular antenna arrays. The method may further include determining, bythe processing system, a DL channel for the UE based on the feedback.

In some implementations of these embodiments, the combination ofcoherent modular antenna arrays serves other UEs in multi-user (Mu)-MIMOmode, where the performing, the identifying, the causing, the obtaining,and the determining enable the combination of coherent modular antennaarrays to transparently serve the UE in Su-MIMO mode and the other UEsin Mu-MIMO mode.

In some implementations of these embodiments, the first polarization andthe third polarization comprise plus (+) 45 degree polarization, wherethe second polarization and the fourth polarization comprise minus (−)45 degree polarization.

In some implementations of these embodiments, the first orthogonal pilotsignal, the second orthogonal pilot signal, the third orthogonal pilotsignal, and the fourth orthogonal pilot signal comprise channel stateinformation (CSI)-reference signals (RS).

In some implementations of these embodiments, the feedback comprises aprecoding matrix indicator (PMI).

In some implementations of these embodiments, the performing the logicalpartition comprises performing a halving partition such that the firstpartition comprises a left partition of the combination of coherentmodular antenna arrays and the second partition comprises a rightpartition of the combination of coherent modular antenna arrays.

FIG. 8N depicts an illustrative embodiment of a method 840 in accordancewith various aspects described herein. In some embodiments, one or moreprocess blocks of FIG. 8N can be performed by a network servicemanagement platform, such as the network service management platform 163a. In some embodiments, one or more process blocks of FIG. 8N may beperformed by another device or a group of devices separate from orincluding the network service management platform 163 a, such as the RIC164 a, the CU 174 a, one or more DUs 166 a, one or more RUs 168 a,and/or the core network 190.

At 840 a, the method can include receiving, over a first interface witha CU, a DU, and an RU of a RAN, first data corresponding to a pluralityof user equipment (UEs), wherein the first data is associated withmonitorable network parameters, wherein the monitorable networkparameters include coherence bandwidth, coherence time, coherence blocksize, or a combination thereof, wherein the RU comprises a combinationof modular antenna arrays, and wherein each modular antenna array of thecombination of modular antenna arrays comprises a respective group ofantenna elements. For example, the system 162 a can, in a manner similarto that described elsewhere herein, perform one or more operations thatinclude receiving, over a first interface with a CU, a DU, and an RU,first data corresponding to a plurality of user equipment (UEs), whereinthe first data is associated with monitorable network parameters, andwherein the monitorable network parameters include coherence bandwidth,coherence time, coherence block size, or a combination thereof.

At 840 b, the method can include providing the first data to anartificial intelligence (AI) model. For example, the system 162 a can,in a manner similar to that described elsewhere herein, perform one ormore operations that include providing the first data to an artificialintelligence (AI) model.

At 840 c, the method can include, responsive to the providing the firstdata to the AI model, obtaining second data from the AI model, whereinthe second data is associated with controllable network parameters, andwherein the controllable network parameters include pilot sequencelength, pilot sequence distribution, setting ofmultiple-input-multiple-output (MIMO) mode, or a combination thereof.For example, the system 162 a can, in a manner similar to that describedelsewhere herein, perform one or more operations that include,responsive to the providing the first data to the AI model, obtainingsecond data from the AI model, wherein the second data is associatedwith controllable network parameters, and wherein the controllablenetwork parameters include pilot sequence length, pilot sequencedistribution, setting of multiple-input-multiple-output (MIMO) mode, ora combination thereof.

At 840 d, the method can include causing the second data to be provided,to the CU, over a second interface with the CU to enable use of thesecond data for the plurality of UEs. For example, the system 162 a can,in a manner similar to that described elsewhere herein, perform one ormore operations that include causing the second data to be provided, tothe CU, over a second interface with the CU to enable use of the seconddata for the plurality of UEs.

In some implementations of these embodiments, the monitorable networkparameters further include scheduled UEs, UE spatial separability,indication of MIMO type, downlink (DL) channel quality index (CQI),uplink (UL) signal-to-interference-plus-noise ratio (SINR), error vectormagnitude (EVM), pilot reuse factor, UL covariance, condition number, ora combination thereof.

In some implementations of these embodiments, the controllable networkparameters further include uplink (UL) UE transmit power control,downlink (DL) transmit power allocation, parallel scheduling control,quiescent antenna weights, setting of single-user (Su)-MIMO rank, or acombination thereof.

In some implementations of these embodiments, the RAN conforms to OpenRAN (O-RAN) standards, and the AI model is implemented in the RIC.

In some implementations of these embodiments, the first interfacecomprises an O1 interface.

In some implementations of these embodiments, the second interfacecomprises an A1 interface.

In some implementations of these embodiments, the AI model isimplemented in an rAPP, an xAPP, or a combination thereof.

In some implementations of these embodiments, the combination of modularantenna arrays is operated in multi-user (Mu)-MIMO mode, single-user(Su)-MIMO mode, or a combination thereof.

In some implementations of these embodiments, the DU comprises a virtualDU.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 8N, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In various embodiments, a non-transitory machine-readable medium maycomprise executable instructions that, when executed by a processingsystem of a radio access network (RAN) including a processor, facilitateperformance of operations. The operations may include obtainingnetwork-related data from a centralized unit (CU), a distributed unit(DU), or a remote unit (RU) of the RAN, wherein the network-related datacorresponds to a user equipment (UE), wherein the network-related datais associated with first parameters, and wherein the first parametersare associated with coherence information. The operations may furtherinclude transmitting the network-related data to a machine learning (ML)model. The operations may further include, responsive to thetransmitting the network-related data to the ML model, receiving controldata from the ML model, wherein the control data is associated withsecond parameters, and wherein the second parameters are associated withpilot sequences, multiple-input-multiple-output (MIMO) modes, or acombination thereof. The operations may further include providing thecontrol data to the CU for controlling the UE.

In some implementations of these embodiments, the first parametersfurther relate to scheduling of UEs, spatial separation of UEs, MIMOtype, downlink (DL) channel quality, uplink (UL) signal quality, or acombination thereof.

In some implementations of these embodiments, the second parametersfurther relate to uplink (UL) power control, downlink (DL) powerallocation, parallel scheduling, antenna weights, single-user (Su)-MIMOranking, or a combination thereof.

In some implementations of these embodiments, the RAN conforms to OpenRAN (O-RAN) standards, and the ML model is implemented in a RANintelligent controller (RIC) of the RAN.

In some implementations of these embodiments, the processing systemcomprises a Service Management and Orchestration (SMO) platform.

In various embodiments, a method may include receiving, by a processingsystem including a processor, and over a first interface of a radioaccess network (RAN), first data corresponding to a plurality of userequipment (UEs), wherein the first data is associated with first networkparameters, and wherein the first network parameters include scheduledUEs, UE spatial separability, or a combination thereof. The method mayfurther include transmitting, by the processing system, the first datato an artificial intelligence (AI) model. The method may furtherinclude, responsive to the transmitting the first data to the AI model,obtaining, by the processing system, second data from the AI model,wherein the second data is associated with second network parameters,and wherein the second network parameters include downlink (DL) transmitpower allocation. The method may further include causing, by theprocessing system, the second data to be provided over a secondinterface to a control unit to enable use of the second data for theplurality of UEs.

In some implementations of these embodiments, the RAN conforms to OpenRAN (O-RAN) standards, and the AI model is implemented in a RANintelligent controller (RIC).

In some implementations of these embodiments, the first interfacecomprises an O1 interface.

In some implementations of these embodiments, the second interfacecomprises an A1 interface.

In some implementations of these embodiments, the processing systemcomprises a Service Management and Orchestration (SMO) platform.

FIG. 8P depicts an illustrative embodiment of a method 842 in accordancewith various aspects described herein. In some embodiments, one or moreprocess blocks of FIG. 8P can be performed by a RAN or system, such asthe system 162 a. In some embodiments, one or more process blocks ofFIG. 8P may be performed by another device or a group of devicesseparate from or including the system 162 a, such as the network servicemanagement platform 163 a, the RIC 164 a, the CU 174 a, one or more DUs166 a, one or more RUs 168 a, and/or the core network 190.

At 842 a, the method can include obtaining information regarding anexternal source that might be affected by downlink (DL) transmissionsemitted by an aggregation of modular antenna arrays, wherein eachmodular antenna array of the aggregation of modular antenna arrayscomprises a set of antenna elements, resulting in multiple sets ofantenna elements, and wherein the aggregation of modular antenna arraysis operated in multi-user (Mu)-multiple-input-multiple-output (MIMO)mode in which parallel transmissions are facilitated for a plurality ofuser equipment (UEs). For example, the system 162 a can, in a mannersimilar to that described elsewhere herein, perform one or moreoperations that include obtaining information regarding an externalsource that might be affected by downlink (DL) transmissions emitted byan aggregation of modular antenna arrays, wherein each modular antennaarray of the aggregation of modular antenna arrays comprises a set ofantenna elements, resulting in multiple sets of antenna elements, andwherein the aggregation of modular antenna arrays is operated inmulti-user (Mu)-multiple-input-multiple-output (MIMO) mode in whichparallel transmissions are facilitated for a plurality of user equipment(UEs).

At 842 b, the method can include determining adjustments for particularantenna elements of the multiple sets of antenna elements based on theobtaining the information, wherein the determining the adjustments isbased on a probability that interference of the parallel transmissionswith the external source will be mitigated. For example, the system 162a can, in a manner similar to that described elsewhere herein, performone or more operations that include determining adjustments forparticular antenna elements of the multiple sets of antenna elementsbased on the obtaining the information, wherein the determining theadjustments is based on a probability that interference of the paralleltransmissions with the external source will be mitigated.

At 842 c, the method can include causing the particular antenna elementsto be operated based on the adjustments. For example, the system 162 acan, in a manner similar to that described elsewhere herein, perform oneor more operations that include causing the particular antenna elementsto be operated based on the adjustments.

In some implementations of these embodiments, the external sourcecomprises an Earth station or a repeater.

In some implementations of these embodiments, the information comprisesgeolocation information for the external source.

In some implementations of these embodiments, the information comprisesdetected uplink (UL) interference associated with the external source.

In some implementations of these embodiments, the information comprisescovariance measurements of one or more signals received over an uplink(UL).

In some implementations of these embodiments, the covariancemeasurements are made after pilot removal from the one or more signals.

In some implementations of these embodiments, the adjustments involvesteering of the parallel transmissions away from the external source.

In some implementations of these embodiments, the adjustments includeprecoding for one or more null patterns to be directed towards theexternal source.

In some implementations of these embodiments, communications between theaggregation of modular antenna arrays and each UE of the plurality ofUEs are in frequency division duplex (FDD).

In some implementations of these embodiments, communications between theaggregation of modular antenna arrays and each UE of the plurality ofUEs are in time division duplex (TDD).

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 8P, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In various embodiments, a device may comprise a processing systemincluding a processor, wherein the processing system is communicativelycoupled with a plurality of coherent modular antenna panels, whereineach modular antenna panel of the plurality of coherent modular antennapanels comprises a group of antenna elements, resulting in multiplegroups of antenna elements, and wherein the plurality of coherentmodular antenna panels is operated in frequency division duplex (FDD)multi-user (Mu)-multiple-input-multiple-output (MIMO) in which paralleltransmissions are facilitated for a plurality of user equipment (UEs).The device may further comprise a memory that stores executableinstructions that, when executed by the processing system, facilitateperformance of operations. The operations may include detecting anexternal noise source. The operations may further include identifyingprecoding for particular antenna elements of the multiple groups ofantenna elements based on the detecting the external noise source. Theoperations may further include operating the particular antenna elementsbased on the precoding when facilitating the parallel transmissions forthe plurality of UEs.

In some implementations of these embodiments, the external noise sourcecomprises an Earth station or a repeater.

In some implementations of these embodiments, the detecting the externalnoise source is based on covariance measurements of one or more signalsreceived over an uplink (UL).

In some implementations of these embodiments, the precoding enablessteering of the parallel transmissions away from the external noisesource.

In some implementations of these embodiments, the precoding enables oneor more null patterns to be directed towards the external noise source.

In various embodiments, a method may include receiving, by a processingsystem including a processor, data regarding an external noise sourcethat is affected by out-of-band downlink (DL) emissions radiated by acoherent combination of modular antenna arrays, wherein each modularantenna array of the coherent combination of modular antenna arrayscomprises a group of antenna elements, resulting in multiple groups ofantenna elements, and wherein the coherent combination of modularantenna arrays is being operated in multi-user(Mu)-multiple-input-multiple-output (MIMO) mode in which paralleltransmissions are facilitated for a plurality of user equipment (UEs).The method may further include identifying, by the processing system,adjustments for select antenna elements of the multiple groups ofantenna elements based on the receiving the data. The method may furtherinclude causing, by the processing system, the select antenna elementsto be operated based on the adjustments such that the paralleltransmissions facilitated for the plurality of UEs are steered away fromthe external noise source.

In some implementations of these embodiments, the external noise sourcecomprises an Earth station or a repeater.

In some implementations of these embodiments, the data relates todetected UL interference associated with the external noise source.

In some implementations of these embodiments, the adjustments includeprecoding for one or more null patterns for the external noise source.

In some implementations of these embodiments, communications between thecoherent combination of modular antenna arrays and each UE of theplurality of UEs are in frequency division duplex (FDD).

FIG. 8Q depicts an illustrative embodiment of a method 844 in accordancewith various aspects described herein. In some embodiments, one or moreprocess blocks of FIG. 8Q can be performed by a RAN or system, such asthe system 162 a. In some embodiments, one or more process blocks ofFIG. 8Q may be performed by another device or a group of devicesseparate from or including the system 162 a, such as the network servicemanagement platform 163 a, the RIC 164 a, the CU 174 a, one or more DUs166 a, one or more RUs 168 a, and/or the core network 190.

At 844 a, the method can include performing a beam sweep in a downlink(DL) using an aggregation of modular antenna arrays, wherein eachmodular antenna array of the aggregation of modular antenna arrayscomprises a set of antenna elements, resulting in multiple sets ofantenna elements, and wherein the aggregation of modular antenna arraysis operated in multi-user (Mu)-multiple-input-multiple-output (MIMO)mode in which parallel transmissions are facilitated for a plurality ofuser equipment (UEs). For example, the system 162 a can, in a mannersimilar to that described elsewhere herein, perform one or moreoperations that include performing a beam sweep in a downlink (DL) usingan aggregation of modular antenna arrays, wherein each modular antennaarray of the aggregation of modular antenna arrays comprises a set ofantenna elements, resulting in multiple sets of antenna elements, andwherein the aggregation of modular antenna arrays is operated inmulti-user (Mu)-multiple-input-multiple-output (MIMO) mode in whichparallel transmissions are facilitated for a plurality of user equipment(UEs).

At 844 b, the method can include, after the performing the beam sweep,receiving, in an uplink (UL), a passive intermodulation (PIM) responseassociated with a PIM source. For example, the system 162 a can, in amanner similar to that described elsewhere herein, perform one or moreoperations that include, after the performing the beam sweep, receiving,in an uplink (UL), a passive intermodulation (PIM) response associatedwith a PIM source.

At 844 c, the method can include determining adjustments for particularantenna elements of the multiple sets of antenna elements based on thePIM response. For example, the system 162 a can, in a manner similar tothat described elsewhere herein, perform one or more operations thatinclude determining adjustments for particular antenna elements of themultiple sets of antenna elements based on the PIM response.

At 844 d, the method can include causing the particular antenna elementsto be operated based on the adjustments when facilitating the paralleltransmissions for the plurality of UEs. For example, the system 162 acan, in a manner similar to that described elsewhere herein, perform oneor more operations that include causing the particular antenna elementsto be operated based on the adjustments when facilitating the paralleltransmissions for the plurality of UEs.

In some implementations of these embodiments, the performing the beamsweep comprises performing the beam sweep in orthogonal beam directions.

In some implementations of these embodiments, the determining theadjustments comprises identifying a direction or location of the PIMsource based on the PIM response.

In some implementations of these embodiments, the causing the particularantenna elements to be operated based on the adjustments enables theparallel transmissions to avoid the PIM source, thereby reducing oreliminating undesired reflections of the parallel transmissions from thePIM source.

In some implementations of these embodiments, the adjustments includecalculations for one or more null patterns towards the PIM source,thereby increasing uplink (UL) coverage.

In some implementations of these embodiments, the one or more nullpatterns comprises one or more quiescent beam patterns that areconcatenated with adaptive beams associated with the paralleltransmissions.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include obtaining measurementsrelating to the PIM response, where the determining the adjustments isbased on the measurements relating to the PIM response.

In some implementations of these embodiments, communications between theaggregation of modular antenna arrays and each UE of the plurality ofUEs are in frequency division duplex (FDD).

In some implementations of these embodiments, the aggregation of modularantenna arrays is further operated in single-user (Su)-MIMO mode.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 8Q, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In various embodiments, a device may comprise a processing systemincluding a processor, wherein the processing system is communicativelycoupled with a plurality of coherent modular antenna panels, whereineach modular antenna panel of the plurality of coherent modular antennapanels comprises a group of antenna elements, resulting in multiplegroups of antenna elements, and wherein the plurality of coherentmodular antenna panels is operated in frequency division duplex (FDD)multi-user (Mu)-multiple-input-multiple-output (MIMO) in which paralleltransmissions are facilitated for a plurality of user equipment (UEs).The device may further comprise a memory that stores executableinstructions that, when executed by the processing system, facilitateperformance of operations. The operations may include causing theplurality of coherent modular antenna panels to conduct, in a downlink(DL), a beam sweep in orthogonal directions. The operations may furtherinclude detecting, in an uplink (UL), a passive intermodulation (PIM)response corresponding to a PIM source. The operations may furtherinclude determining measurement data based on the PIM response. Theoperations may further include operating particular antenna elements ofthe multiple groups of antenna elements based on the measurement datawhen facilitating the parallel transmissions for the plurality of UEs.

In some implementations of these embodiments, the determining themeasurement data comprises identifying a direction or location of thePIM source based on the PIM response.

In some implementations of these embodiments, the operating theparticular antenna elements based on the measurement data enables theparallel transmissions to avoid the PIM source, thereby reducing oreliminating undesired reflections of the parallel transmissions from thePIM source.

In some implementations of these embodiments, the operating theparticular antenna elements comprises applying one or more null patternstowards the PIM source, thereby increasing uplink (UL) coverage.

In some implementations of these embodiments, the one or more nullpatterns comprises one or more quiescent beam patterns that areconcatenated with adaptive beams associated with the paralleltransmissions.

In various embodiments, a method may include performing, by a processingsystem including a processor, a beam sweep in a downlink (DL) using acombination of coherent modular antenna arrays, wherein each modularantenna array of the combination of coherent modular antenna arrayscomprises a set of antenna elements, resulting in multiple sets ofantenna elements, and wherein the combination of coherent modularantenna arrays is operated in multi-user(Mu)-multiple-input-multiple-output (MIMO) mode in which paralleltransmissions are facilitated for a plurality of user equipment (UEs).The method may further include obtaining, by the processing system viathe multiple sets of antenna elements, a signal in an uplink (UL)responsive to the performing the beam sweep, wherein the signal includescomponents associated with a passive intermodulation (PIM) source. Themethod may further include identifying, by the processing system,adjustments for select antenna elements of the multiple sets of antennaelements based on the signal. The method may further include causing, bythe processing system, the select antenna elements to be operated basedon the adjustments when facilitating the parallel transmissions for theplurality of UEs, wherein the causing enables at least a portion of theparallel transmissions to avoid the PIM source, thereby reducing oreliminating undesired reflections of the parallel transmissions from thePIM source.

In some implementations of these embodiments, the performing the beamsweep comprises performing the beam sweep in orthogonal beam directions.

In some implementations of these embodiments, the identifying theadjustments comprises identifying a direction or location of the PIMsource based on the signal.

In some implementations of these embodiments, the adjustments includecalculations for one or more null patterns towards the PIM source,thereby increasing uplink (UL) coverage.

In some implementations of these embodiments, communications between thecombination of coherent modular antenna arrays and each UE of theplurality of UEs are in frequency division duplex (FDD).

FIG. 8R depicts an illustrative embodiment of a method 846 in accordancewith various aspects described herein. In some embodiments, one or moreprocess blocks of FIG. 8R can be performed by a RAN or system, such asthe system 162 a. In some embodiments, one or more process blocks ofFIG. 8R may be performed by another device or a group of devicesseparate from or including the system 162 a, such as the network servicemanagement platform 163 a, the RIC 164 a, the CU 174 a, one or more DUs166 a, one or more RUs 168 a, and/or the core network 190.

At 846 a, the method can include obtaining, over an uplink (UL) using anaggregation of modular antenna arrays, a modulated signal that includesfeedback transmitted by a user equipment (UE), wherein the aggregationof modular antenna arrays comprises multiple groups of antenna elements.For example, the system 162 a can, in a manner similar to that describedelsewhere herein, perform one or more operations that include obtaining,over an uplink (UL) using an aggregation of modular antenna arrays, amodulated signal that includes feedback transmitted by a user equipment(UE), wherein the aggregation of modular antenna arrays comprisesmultiple groups of antenna elements.

At 846 b, the method can include, after the obtaining the modulatedsignal, performing a demodulation of the modulated signal. For example,the system 162 a can, in a manner similar to that described elsewhereherein, perform one or more operations that include, after the obtainingthe modulated signal, performing a demodulation of the modulated signal.

At 846 c, the method can include determining demodulator constellationerrors from the demodulation of the modulated signal. For example, thesystem 162 a can, in a manner similar to that described elsewhereherein, perform one or more operations that include determiningdemodulator constellation errors from the demodulation of the modulatedsignal.

At 846 d, the method can include performing an error gradient weightadaptation responsive to the determining the demodulator constellationerrors to derive revised weights for various antenna elements of themultiple groups of antenna elements. For example, the system 162 a can,in a manner similar to that described elsewhere herein, perform one ormore operations that include performing an error gradient weightadaptation responsive to the determining the demodulator constellationerrors to derive revised weights for various antenna elements of themultiple groups of antenna elements.

At 846 e, the method can include applying the revised weights to thevarious antenna elements of the multiple groups of antenna elements toadjust signals received over the UL. For example, the system 162 a can,in a manner similar to that described elsewhere herein, perform one ormore operations that include applying the revised weights to the variousantenna elements of the multiple groups of antenna elements to adjustsignals received over the UL.

In some implementations of these embodiments, the determining thedemodulator constellation errors comprises measuring an error vectormagnitude (EVM) based on the demodulation of the modulated signal.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include requesting the UE to providethe feedback, where the obtaining the modulated signal is responsive tothe requesting.

In some implementations of these embodiments, the modulated signalcomprises an orthogonal frequency division multiplexing (OFDM) signal.

In some implementations of these embodiments, the applying the revisedweights compensates for channel estimation errors in the UL.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include determining a coherenceblock for the UE.

In some implementations of these embodiments, the system 162 a mayperform one or more operations that include identifying that thecoherence block for the UE is smaller than a threshold, where thedetermining the demodulator constellation errors, the performing theerror gradient weight adaptation, and the applying the revised weightsare based on the identifying that the coherence block for the UE issmaller than the threshold.

In some implementations of these embodiments, the aggregation of modularantenna arrays operates as a coherent antenna system.

In some implementations of these embodiments, communications between theaggregation of modular antenna arrays and the UE are in frequencydivision duplex (FDD).

In some implementations of these embodiments, communications between theaggregation of modular antenna arrays and the UE are in time divisionduplex (TDD).

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 8R, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

In various embodiments, a device may comprise a processing systemincluding a processor, wherein the processing system is communicativelycoupled with a plurality of coherent modular antenna panels. The devicemay further comprise a memory that stores executable instructions that,when executed by the processing system, facilitate performance ofoperations. The operations may include receiving, via the plurality ofcoherent modular antenna panels, feedback provided by a user equipment(UE), wherein each modular antenna panel of the plurality of coherentmodular antenna panels comprises a set of antenna elements, resulting inmultiple sets of antenna elements. The operations may further includedetermining constellation errors relating to a demodulator bycalculating a root mean square (RMS) of error vectors resulting fromdemodulation of the feedback. The operations may further includecalculating adjusted weights for select antenna elements of the multiplesets of antenna elements based on the constellation errors. Theoperations may further include causing the select antenna elements ofthe multiple sets of antenna elements to operate in accordance with theadjusted weights.

In some implementations of these embodiments, the calculating theadjusted weights comprises performing an error gradient weightadaptation.

In some implementations of these embodiments, the operations may furtherinclude requesting the UE to provide the feedback, and wherein thereceiving the feedback is responsive to the requesting.

In some implementations of these embodiments, the operations may furtherinclude determining a coherence block for the UE.

In some implementations of these embodiments, the operations may furtherinclude identifying that the coherence block for the UE is smaller thana threshold, where the determining the constellation errors, thecalculating the adjusted weights, and the causing the select antennaelements of the multiple sets of antenna elements to operate inaccordance with the adjusted weights are based on the identifying thatthe coherence block for the UE is smaller than the threshold.

In various embodiments, a method may include obtaining, by a processingsystem using a combination of coherent modular antenna arrays, amodulated signal transmitted by a user equipment (UE), wherein thecombination of coherent modular antenna arrays comprises multiple groupsof antenna elements. The method may further include, responsive to theobtaining the modulated signal, demodulating, by the processing system,the modulated signal. The method may further include measuring, by theprocessing system, an error vector magnitude (EVM) based on thedemodulating the modulated signal. The method may further includeperforming, by the processing system, an error gradient weightadaptation responsive to the measuring the EVM to generate adjustedweights for various antenna elements of the multiple groups of antennaelements. The method may further include causing, by the processingsystem, the adjusted weights to be applied to the various antennaelements of the multiple groups of antenna elements to calibrate thevarious antenna elements.

In some implementations of these embodiments, the modulated signalcomprises an orthogonal frequency division multiplexing (OFDM) signal.

In some implementations of these embodiments, the modulated signal istransmitted by the UE while the UE is located at or within a thresholddistance from a boresight of the combination of coherent modular antennaarrays.

In some implementations of these embodiments, the method may furtherinclude storing the adjusted weights for the various antenna elements.

In some implementations of these embodiments, the method may furtherinclude obtaining, by the processing system using the combination ofcoherent modular antenna arrays, a second modulated signal transmittedby a second UE. In some implementations of these embodiments, the methodmay further include, responsive to the obtaining the second modulatedsignal, demodulating, by the processing system, the second modulatedsignal. In some implementations of these embodiments, the method mayfurther include measuring, by the processing system, a second EVM basedon the demodulating the second modulated signal. In some implementationsof these embodiments, the method may further include performing, by theprocessing system, a second error gradient weight adaptation responsiveto the measuring the second EVM to generate additional adjusted weightsfor the various antenna elements of the multiple groups of antennaelements. In some implementations of these embodiments, the method mayfurther include comparing, by the processing system, the adjustedweights and the additional adjusted weights with respect to one or morethresholds. In some implementations of these embodiments, the method mayfurther include determining, by the processing system, to apply theadjusted weights, the additional adjusted weights, or one or moreaverages thereof to the various antenna elements of the multiple groupsof antenna elements based on a result of the comparing.

Referring now to FIG. 9, a block diagram 900 is shown illustrating anexample, non-limiting embodiment of a virtualized communications networkin accordance with various aspects described herein. In particular, avirtualized communications network is presented that can be used toimplement some or all of the subsystems and functions of varioussystems, devices, units, etc. described above. For example, virtualizedcommunications network 900 can, in whole or in part, facilitateoptimization or improvement of service quality and/or capacity in a MIMOnetwork supported by aggregations of modular antenna arrays and/orfacilitate mitigating interference relating to fixed interferers.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 950, a virtualized network function cloud 925 and/or oneor more cloud computing environments 975. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationsnetwork employs virtual network elements (VNEs) 930, 932, 934, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or general purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1A),such as an edge router can be implemented via a VNE 930 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it'selastic: so the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle-boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing infrastructure easier to manage.

In an embodiment, the transport layer 950 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 159 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized, and might require special DSP code andanalog front-ends (AFEs) that do not lend themselves to implementationas VNEs 930, 932 or 934. These network elements can be included intransport layer 950.

The virtualized network function cloud 925 interfaces with the transportlayer 950 to provide the VNEs 930, 932, 934, etc. to provide specificNFVs. In particular, the virtualized network function cloud 925leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 930, 932 and 934can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 930, 932 and 934 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements don't typically need toforward large amounts of traffic, their workload can be distributedacross a number of servers—each of which adds a portion of thecapability, and overall which creates an elastic function with higheravailability than its former monolithic version. These virtual networkelements 930, 932, 934, etc. can be instantiated and managed using anorchestration approach similar to those used in cloud compute services.

The cloud computing environments 975 can interface with the virtualizednetwork function cloud 925 via APIs that expose functional capabilitiesof the VNEs 930, 932, 934, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 925. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 925 and cloud computingenvironment 975 and in the commercial cloud, or might simply orchestrateworkloads supported entirely in NFV infrastructure from these thirdparty locations.

Turning now to FIG. 10, there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 10 and the followingdiscussion are intended to provide a brief, general description of asuitable computing environment 1000 in which the various embodiments ofthe subject disclosure can be implemented. In particular, computingenvironment 1000 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or VNEs 930, 932,934, etc. Each of these devices can be implemented viacomputer-executable instructions that can run on one or more computers,and/or in combination with other program modules and/or as a combinationof hardware and software. For example, computing environment 1000 can,in whole or in part, facilitate optimization or improvement of servicequality and/or capacity in a MIMO network supported by aggregations ofmodular antenna arrays and/or facilitate mitigating interferencerelating to fixed interferers.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 10, the example environment can comprise acomputer 1002, the computer 1002 comprising a processing unit 1004, asystem memory 1006 and a system bus 1008. The system bus 1008 couplessystem components including, but not limited to, the system memory 1006to the processing unit 1004. The processing unit 1004 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 1004.

The system bus 1008 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1006comprises ROM 1010 and RAM 1012. A basic input/output system (BIOS) canbe stored in a non-volatile memory such as ROM, erasable programmableread only memory (EPROM), EEPROM, which BIOS contains the basic routinesthat help to transfer information between elements within the computer1002, such as during startup. The RAM 1012 can also comprise ahigh-speed RAM such as static RAM for caching data.

The computer 1002 further comprises an internal hard disk drive (HDD)1014 (e.g., EIDE, SATA), which internal HDD 1014 can also be configuredfor external use in a suitable chassis (not shown), a magnetic floppydisk drive (FDD) 1016, (e.g., to read from or write to a removablediskette 1018) and an optical disk drive 1020, (e.g., reading a CD-ROMdisk 1022 or, to read from or write to other high capacity optical mediasuch as the DVD). The HDD 1014, magnetic FDD 1016 and optical disk drive1020 can be connected to the system bus 1008 by a hard disk driveinterface 1024, a magnetic disk drive interface 1026 and an opticaldrive interface 1028, respectively. The hard disk drive interface 1024for external drive implementations comprises at least one or both ofUniversal Serial Bus (USB) and Institute of Electrical and ElectronicsEngineers (IEEE) 1394 interface technologies. Other external driveconnection technologies are within contemplation of the embodimentsdescribed herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1002, the drives andstorage media accommodate the storage of any data in a suitable digitalformat. Although the description of computer-readable storage mediaabove refers to a hard disk drive (HDD), a removable magnetic diskette,and a removable optical media such as a CD or DVD, it should beappreciated by those skilled in the art that other types of storagemedia which are readable by a computer, such as zip drives, magneticcassettes, flash memory cards, cartridges, and the like, can also beused in the example operating environment, and further, that any suchstorage media can contain computer-executable instructions forperforming the methods described herein.

A number of program modules can be stored in the drives and RAM 1012,comprising an operating system 1030, one or more application programs1032, other program modules 1034 and program data 1036. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1012. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 1002 throughone or more wired/wireless input devices, e.g., a keyboard 1038 and apointing device, such as a mouse 1040. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 1004 through aninput device interface 1042 that can be coupled to the system bus 1008,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 1044 or other type of display device can be also connected tothe system bus 1008 via an interface, such as a video adapter 1046. Itwill also be appreciated that in alternative embodiments, a monitor 1044can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 1002 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 1044, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 1002 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1048. The remotecomputer(s) 1048 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer1002, although, for purposes of brevity, only a remote memory/storagedevice 1050 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 1052 and/orlarger networks, e.g., a wide area network (WAN) 1054. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 1002 can beconnected to the LAN 1052 through a wired and/or wireless communicationsnetwork interface or adapter 1056. The adapter 1056 can facilitate wiredor wireless communication to the LAN 1052, which can also comprise awireless AP disposed thereon for communicating with the adapter 1056.

When used in a WAN networking environment, the computer 1002 cancomprise a modem 1058 or can be connected to a communications server onthe WAN 1054 or has other means for establishing communications over theWAN 1054, such as by way of the Internet. The modem 1058, which can beinternal or external and a wired or wireless device, can be connected tothe system bus 1008 via the input device interface 1042. In a networkedenvironment, program modules depicted relative to the computer 1002 orportions thereof, can be stored in the remote memory/storage device1050. It will be appreciated that the network connections shown areexample and other means of establishing a communications link betweenthe computers can be used.

The computer 1002 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 11, an embodiment 1100 of a mobile network platform1110 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 930, 932, 934, etc. For example, platform 1110 can, in wholeor in part, facilitate optimization or improvement of service qualityand/or capacity in a MIMO network supported by aggregations of modularantenna arrays and/or facilitate mitigating interference relating tofixed interferers. In one or more embodiments, the mobile networkplatform 1110 can generate and receive signals transmitted and receivedby base stations or access points such as base station or access point122. Generally, mobile network platform 1110 can comprise components,e.g., nodes, gateways, interfaces, servers, or disparate platforms, thatfacilitate both packet-switched (PS) (e.g., internet protocol (IP),frame relay, asynchronous transfer mode (ATM)) and circuit-switched (CS)traffic (e.g., voice and data), as well as control generation fornetworked wireless telecommunication. As a non-limiting example, mobilenetwork platform 1110 can be included in telecommunications carriernetworks, and can be considered carrier-side components as discussedelsewhere herein. Mobile network platform 1110 comprises CS gatewaynode(s) 1112 which can interface CS traffic received from legacynetworks like telephony network(s) 1140 (e.g., public switched telephonenetwork (PSTN), or public land mobile network (PLMN)) or a signalingsystem #7 (SS7) network 1160. CS gateway node(s) 1112 can authorize andauthenticate traffic (e.g., voice) arising from such networks.Additionally, CS gateway node(s) 1112 can access mobility, or roaming,data generated through SS7 network 1160; for instance, mobility datastored in a visited location register (VLR), which can reside in memory1130. Moreover, CS gateway node(s) 1112 interfaces CS-based traffic andsignaling and PS gateway node(s) 1118. As an example, in a 3GPP UMTSnetwork, CS gateway node(s) 1112 can be realized at least in part ingateway GPRS support node(s) (GGSN). It should be appreciated thatfunctionality and specific operation of CS gateway node(s) 1112, PSgateway node(s) 1118, and serving node(s) 1116, is provided and dictatedby radio technology(ies) utilized by mobile network platform 1110 fortelecommunication over a radio access network 1120 with other devices,such as a radiotelephone 1175.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 1118 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 1110, like wide area network(s) (WANs) 1150,enterprise network(s) 1170, and service network(s) 1180, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 1110 through PS gateway node(s) 1118. It is tobe noted that WANs 1150 and enterprise network(s) 1170 can embody, atleast in part, a service network(s) like IP multimedia subsystem (IMS).Based on radio technology layer(s) available in technology resource(s)or radio access network 1120, PS gateway node(s) 1118 can generatepacket data protocol contexts when a data session is established; otherdata structures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 1118 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 1100, mobile network platform 1110 also comprises servingnode(s) 1116 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 1120, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 1118. It is to be noted that for technology resource(s) thatrely primarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 1118; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 1116 can be embodied in servingGPRS support node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)1114 in mobile network platform 1110 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 1110. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 1118 for authorization/authentication and initiation of a datasession, and to serving node(s) 1116 for communication thereafter. Inaddition to application server, server(s) 1114 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 1110 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 1112and PS gateway node(s) 1118 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 1150 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 1110 (e.g., deployed and operated by the same serviceprovider), such as distributed antenna networks that enhance wirelessservice coverage by providing more network coverage.

It is to be noted that server(s) 1114 can comprise one or moreprocessors configured to confer at least in part the functionality ofmobile network platform 1110. To that end, the one or more processor canexecute code instructions stored in memory 1130, for example. It shouldbe appreciated that server(s) 1114 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 1100, memory 1130 can store information related tooperation of mobile network platform 1110. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 1110, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 1130 can also storeinformation from at least one of telephony network(s) 1140, WAN 1150,SS7 network 1160, or enterprise network(s) 1170. In an aspect, memory1130 can be, for example, accessed as part of a data store component oras a remotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 11, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 12, an illustrative embodiment of a communicationdevice 1200 is shown. The communication device 1200 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,computing device 1200 can, in whole or in part, facilitate optimizationor improvement of service quality and/or capacity in a MIMO networksupported by aggregations of modular antenna arrays and/or facilitatemitigating interference relating to fixed interferers.

The communication device 1200 can comprise a wireline and/or wirelesstransceiver 1202 (herein transceiver 1202), a user interface (UI) 1204,a power supply 1214, a location receiver 1216, a motion sensor 1218, anorientation sensor 1220, and a controller 1206 for managing operationsthereof. The transceiver 1202 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 1202 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 1204 can include a depressible or touch-sensitive keypad 1208with a navigation mechanism such as a roller ball, a joystick, a mouse,or a navigation disk for manipulating operations of the communicationdevice 1200. The keypad 1208 can be an integral part of a housingassembly of the communication device 1200 or an independent deviceoperably coupled thereto by a tethered wireline interface (such as a USBcable) or a wireless interface supporting for example Bluetooth®. Thekeypad 1208 can represent a numeric keypad commonly used by phones,and/or a QWERTY keypad with alphanumeric keys. The UI 1204 can furtherinclude a display 1210 such as monochrome or color LCD (Liquid CrystalDisplay), OLED (Organic Light Emitting Diode) or other suitable displaytechnology for conveying images to an end user of the communicationdevice 1200. In an embodiment where the display 1210 is touch-sensitive,a portion or all of the keypad 1208 can be presented by way of thedisplay 1210 with navigation features.

The display 1210 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 1200 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 1210 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 1210 can be an integral part of the housingassembly of the communication device 1200 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 1204 can also include an audio system 1212 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 1212 can further include amicrophone for receiving audible signals of an end user. The audiosystem 1212 can also be used for voice recognition applications. The UI1204 can further include an image sensor 1213 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 1214 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 1200 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 1216 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 1200 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor1218 can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 1200 in three-dimensional space. Theorientation sensor 1220 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device1200 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 1200 can use the transceiver 1202 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 1206 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 1200.

Other components not shown in FIG. 12 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 1200 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).Additionally, the disclosed memory components of systems or methodsherein are intended to comprise, without being limited to comprising,these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can begenerated including services being accessed, media consumption history,user preferences, and so forth. This information can be obtained byvarious methods including user input, detecting types of communications(e.g., video content vs. audio content), analysis of content streams,sampling, and so forth. The generating, obtaining and/or monitoring ofthis information can be responsive to an authorization provided by theuser. In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically identifying acquired cell sites that provide a maximumvalue/benefit after addition to an existing communications network) canemploy various AI-based schemes for carrying out various embodimentsthereof. Moreover, the classifier can be employed to determine a rankingor priority of each cell site of the acquired network. A classifier is afunction that maps an input attribute vector, x=(x1, x2, x3, x4, . . . ,xn), to a confidence that the input belongs to a class, that is,f(x)=confidence (class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determine or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hypersurface in the space of possible inputs, which thehypersurface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachescomprise, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunications network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A non-transitory machine-readable medium,comprising executable instructions that, when executed by a processingsystem operatively coupled to an aggregation of modular antenna arraysand including a processor, facilitate performance of operations, theoperations comprising: obtaining information regarding an externalsource that might be affected by downlink (DL) transmissions emitted bythe aggregation of modular antenna arrays, wherein each modular antennaarray of the aggregation of modular antenna arrays comprises a set ofantenna elements, resulting in multiple sets of antenna elements, andwherein the aggregation of modular antenna arrays is operated inmulti-user (Mu)-multiple-input-multiple-output (MIMO) mode in whichparallel transmissions are facilitated for a plurality of user equipment(UEs); determining adjustments for particular antenna elements of themultiple sets of antenna elements based on the obtaining theinformation, wherein the determining the adjustments is based on aprobability that interference of the parallel transmissions with theexternal source will be mitigated; and causing the particular antennaelements to be operated based on the adjustments.
 2. The non-transitorymachine-readable medium of claim 1, wherein the external sourcecomprises an Earth station or a repeater.
 3. The non-transitorymachine-readable medium of claim 1, wherein the information comprisesgeolocation information for the external source.
 4. The non-transitorymachine-readable medium of claim 1, wherein the information comprisesdetected uplink (UL) interference associated with the external source.5. The non-transitory machine-readable medium of claim 1, wherein theinformation comprises covariance measurements of one or more signalsreceived over an uplink (UL).
 6. The non-transitory machine-readablemedium of claim 5, wherein the covariance measurements are made afterpilot removal from the one or more signals.
 7. The non-transitorymachine-readable medium of claim 1, wherein the adjustments involvesteering of the parallel transmissions away from the external source. 8.The non-transitory machine-readable medium of claim 1, wherein theadjustments include precoding or combining for one or more null patternsto be directed towards the external source, wherein the informationcomprises known geolocation of the external source, and wherein thedetermining the adjustments involves adding known data to anartificially-developed expectation of spatial correlations.
 9. Thenon-transitory machine-readable medium of claim 1, whereincommunications between the aggregation of modular antenna arrays andeach UE of the plurality of UEs are in frequency division duplex (FDD).10. The non-transitory machine-readable medium of claim 1, whereincommunications between the aggregation of modular antenna arrays andeach UE of the plurality of UEs are in time division duplex (TDD).
 11. Adevice, comprising: a processing system including a processor, whereinthe processing system is communicatively coupled with a plurality ofcoherent modular antenna panels, wherein each modular antenna panel ofthe plurality of coherent modular antenna panels comprises a group ofantenna elements, resulting in multiple groups of antenna elements, andwherein the plurality of coherent modular antenna panels is operated infrequency division duplex (FDD) multi-user(Mu)-multiple-input-multiple-output (MIMO) in which paralleltransmissions are facilitated for a plurality of user equipment (UEs);and a memory that stores executable instructions that, when executed bythe processing system, facilitate performance of operations, theoperations comprising: detecting an external noise source; identifyingprecoding for particular antenna elements of the multiple groups ofantenna elements based on the detecting the external noise source; andoperating the particular antenna elements based on the precoding whenfacilitating the parallel transmissions for the plurality of UEs. 12.The device of claim 11, wherein the external noise source comprises anEarth station or a repeater.
 13. The device of claim 11, wherein thedetecting the external noise source is based on covariance measurementsof one or more signals received over an uplink (UL).
 14. The device ofclaim 11, wherein the precoding enables steering of the paralleltransmissions away from the external noise source.
 15. The device ofclaim 11, wherein the precoding enables one or more null patterns to bedirected towards the external noise source.
 16. A method, comprising:receiving, by a processing system including a processor, data regardingan external noise source that is affected by out-of-band downlink (DL)emissions radiated by a coherent combination of modular antenna arrays,wherein each modular antenna array of the coherent combination ofmodular antenna arrays comprises a group of antenna elements, resultingin multiple groups of antenna elements, and wherein the coherentcombination of modular antenna arrays is being operated in multi-user(Mu)-multiple-input-multiple-output (MIMO) mode in which paralleltransmissions are facilitated for a plurality of user equipment (UEs);identifying, by the processing system, adjustments for select antennaelements of the multiple groups of antenna elements based on thereceiving the data; and causing, by the processing system, the selectantenna elements to be operated based on the adjustments such that theparallel transmissions facilitated for the plurality of UEs are steeredaway from the external noise source.
 17. The method of claim 16, whereinthe external noise source comprises an Earth station or a repeater. 18.The method of claim 16, wherein the data relates to detected ULinterference associated with the external noise source.
 19. The methodof claim 16, wherein the adjustments include precoding for one or morenull patterns for the external noise source.
 20. The method of claim 16,wherein communications between the coherent combination of modularantenna arrays and each UE of the plurality of UEs are in frequencydivision duplex (FDD).